Emerging jurisprudential challenges of legal accountability and liability.
Abdallah Mrindoko Ally1
This study examines the emerging jurisprudential challenges posed by AI-powered autonomous decision-making systems and assesses the adequacy of existing legal frameworks in addressing issues of accountability, liability, discrimination, negligence, and rights violations arising from their use. The rapid advancement of Artificial Intelligence (AI) has transformed social, economic, and legal relations worldwide, with AI-driven systems increasingly deployed in critical sectors such as healthcare, finance, education, employment, transportation, public administration, and digital commerce. These systems perform functions traditionally reserved for human judgment, offering significant benefits in efficiency, accuracy, productivity, and innovation. Despite these advantages, the growing autonomy of AI technologies raises complex legal concerns regarding accountability, transparency, liability, and the protection of fundamental rights. Particular challenges emerge from algorithmic bias, opaque decision-making processes, data protection risks, and the tension between technological innovation and legal certainty. The study adopts a doctrinal legal research methodology to critically evaluate Tanzanian statutory and regulatory frameworks, including laws governing finance, education, and labour, in determining their capacity to address liability issues associated with autonomous AI systems. A comparative approach is further employed to examine international standards and best practices relevant to AI governance and accountability. The paper argues that the increasing deployment of autonomous AI systems necessitates a reconceptualisation of traditional legal notions of personality, accountability, and liability. It concludes that adaptive and responsive regulatory frameworks are essential to safeguard justice, human rights, consumer protection, and public trust while promoting responsible technological innovation in the digital era.
Integrating market analytics, consumer psychology, and operational intelligence
Tharaa Mahmoud Al Khodor1
Emerging pharma-cosmetic enterprises operate at the intersection of two historically distinct industries—regulated pharmaceutical science and fast-moving consumer beauty markets—yet most of these firms still rely on fragmented spreadsheets, intuition-driven forecasting, and siloed departmental reporting to guide strategic decisions. This paper proposes and conceptually validates an integrated Artificial Intelligence (AI) decision-support framework that unifies three previously disconnected intelligence streams: market analytics, consumer psychology, and operational intelligence. Using a conceptual-analytical methodology grounded in a systematic synthesis of 40 peer-reviewed and industry sources, the study examines how machine learning-based market trend mining, natural language processing-driven sentiment analysis, and predictive operational forecasting can be architected into a single closed-loop pipeline that transforms raw, multi-source data into actionable strategic intelligence for pharma-cosmetic (cosmeceutical) executives. The proposed framework—termed the Integrated Pharma-Cosmetic Intelligence Pipeline (IPCIP)—comprises five layers: data acquisition, AI-driven analytical processing, explainable decision synthesis, human-AI collaborative governance, and continuous feedback learning. Findings synthesized from the literature indicate that AI integration can materially improve forecast accuracy, reduce stock-outs, surface latent consumer sentiment drivers invisible to manual analysis, and shorten the strategic decision cycle, while simultaneously introducing new risks related to interpretability, data fragmentation, and regulatory compliance unique to regulated cosmeceutical products. The paper concludes that AI-driven decision-making does not replace managerial judgment but augments it through transparent, explainable, and continuously learning systems. Limitations, practical recommendations, and directions for future empirical validation are discussed.
Antoine Trad1
The hyper-evolution of Artificial Intelligence (AI), business needs, enterprise requirements, and information technology is provoking accelerated disruptions and transformation failures. Therefore, businesses have to implement business transformation projects that demand profound AI-based changes, and academic organizations must offer adapted educational curricula. Such projects start with the digital transformation phase, known as digitization, that depend mainly on the changes related to customers' demands and behaviors. Businesses have to be fast in adapting to new realities, where they have to completely rethink their business strategy, business models, (re)structure organizational models, review working models, and adopt information technology design models and approaches, like the database models (Tegborg, 2024). As databases are central components, an AI-based Transformation Project (AITP) and digitization initiatives can adopt a DataBase (DB) Generic and Centric Concept (DBGCC). AITP problems can be solved by applying another level of indirection, which results in multi-tier architectures, leaving no space for other types of architectures and compound patterns. Businesses need to develop distributed applications that are based on DBs and even apply a DBGCC using the latest avant-garde technologies, DB Refined Compound Patterns (DBRCP), and the right approach.
Antoine Trad1
This article's main focus is on Polymathic and/or Complex Transformation Projects (PCTP) and on the roles of Artificial Intelligence (AI) and Trustworthy AI (TAI) in educational cycles. Where TAI adopts mainly a qualitative method founded on the Problem-based Learning (PBL) approach in the context of Project-based Learning (PrBL). But PCTPs are of major importance for academic institutions, research centers, and business organizations. Unfortunately, PCTPs are very complex and have extremely high failure rates, mainly because of the lack of a profile that has a Polymathical mindset, real-world interdisciplinary, information technology, and TAI background, expertise, capabilities, and skills. The AIPBLI proposes a Polymathics-based academic, educational, and research (and development) approach, which is associated with a curriculum and a roadmap. Optimally, such a profile is detected in the Primary or the Secondary schools or Cycles (PSC), then is enhanced in the University or Higher-education Cycles (UHC) (Bachelor, Master, and Doctorate levels). Later in the person's Professional Life Cycle (PLC), there is a need for continuous upskilling periods (Trad, 2025a,2026a). PCTPs need an In-house Implemented (II) Education-Centric (EC) Transformation (and Adoption) Framework (IIHCTF) in which Natural Language (NL) and related Processing (NLP) environments are used to define, (re)engineer, integrate, and manage PBLs in the contexts of PrBLs. PBLs in PCTPs (and other types of common projects) are associated with specific PrBLs' requests, needs, data environments, requirements, and problems to be solved.
Sharanagoud S Biradar1; Renuka Guttedar2; Triveni3
AI-driven personalization in service marketing has revolutionized endorsement systems by delivering extremely precise recommendations tailored to separate preferences. Conversely, this precision frequently restricts users within filter bubbles, decreasing exposure to novel experiences and restrictive emotional engagement, which can render relations transactional. Addressing this issue, AI-driven digital serendipity familiarises measured randomness into recommendation algorithms, fostering meaningful unexpected detections that improve engagement, consumer delight, and long-term brand loyalty. An experimental design related a standard predictive system with an AI model integrating stochastic divergence nodes. Participants were randomly dispersed through service domains, getting stimuli during key exploration stages. The commendation logic balanced significance and novelty using a calibrated stochasticity ratio. Behavioural metrics such as dwell time, click-through rates, and navigation patterns were restrained alongside Likert-scale assessments of satisfaction and emotional delight. Statistical analyses comprising mediation analysis, regression, and ANOVA assessed relationships, group differences, and the impact of serendipitous references. Consequences confirmed that AI-driven serendipity suggestively enhanced engagement and satisfaction: click-through rates rose from 12.4% to 20.8%, dwell time increased from 4.2 to 7.1 minutes, exploration depth grew by 85.7%, and "lucky find" experiences extended 34.3%. Emotional delight scores enhanced from 3.2 to 4.4, and brand advocacy rose to 62%, with regression analyses showing strong effects (β = 0.38, Adj. R² = 0.78). Despite these implications, future research must discuss long-term behavioural validation, ethical suggestions, cross-cultural applicability, and dynamic optimization of stochasticity over various service environments to sustain meaningful engagement.
Governance, regulation, and the future of work in the age of artificial intelligence
Monisha J1
Artificial Intelligence (AI) is reshaping economies, workplaces, and societies by driving innovation, improving productivity, and creating new opportunities for growth. However, the rapid adoption of AI also raises significant concerns regarding human rights, privacy, fairness, accountability, and the future of employment. This paper examines the challenge of balancing technological innovation with the protection of fundamental human rights in an increasingly AI-driven world. The study explores the role of governments, regulatory institutions, and organizations in developing governance frameworks that promote responsible AI while ensuring transparency, inclusivity, and respect for human dignity. It reviews emerging regulatory approaches, including risk-based AI governance models, data protection regulations, and ethical AI guidelines implemented across different jurisdictions. Particular attention is given to the impact of AI on labour markets, changing skill requirements, workforce transformation, and workers' rights. The paper argues that human-centred AI governance is essential to address issues such as algorithmic bias, workplace surveillance, job displacement, and unequal access to technological benefits. It further emphasizes the need for collaboration among policymakers, industry leaders, educational institutions, and civil society to build sustainable and equitable AI ecosystems. The study concludes that effective AI regulation should not hinder innovation but rather strengthen public trust, encourage responsible technological development, and support long-term social and economic progress. Integrating human rights principles into AI governance and employment policies can help ensure that technological advancement contributes to inclusive growth, social justice, and improved quality of life for present and future generations.
Navigating accountability, law, and labour in an AI-driven society
Nisha Wadhawan1; Swati Mathur2
Artificial Intelligence (AI) systems increasingly participate in decisions that carry legal, economic, and civic weight; from hiring and credit scoring to policing and public administration. As these systems take on roles once reserved for human judgment, questions of accountability, rights protection, and labour impact have become central to responsible AI governance. This paper examines the evolving regulatory landscape surrounding AI, focusing on the tension between innovation and oversight, the "responsibility gap" created by autonomous decision-making, and the consequences of algorithmic governance for workers' rights and data privacy. Drawing on comparative analysis of the European Union's Artificial Intelligence Act, the United States' sectoral approach, and emerging international frameworks, the paper identifies structural challenges to enforcement, including regulatory lag, cross-border inconsistency, and the opacity of proprietary systems. It concludes that durable AI governance requires harmonized international standards, ethics-by-design practices, and sustained collaboration between regulators, technologists, and legal scholars to ensure that AI development remains accountable to the rights and livelihoods of the people it affects.
Implications for education and research
Megha Ojha1
The introduction of Generative Artificial Intelligence (AI) technologies has transformed the processes of creation, dissemination, and consumption of knowledge in education and research. AI systems can generate text, images, sound, software code, and creative content. Thus, AI has opened up new avenues for innovation, academic research, and information dissemination. However, the quick adoption of AI technology has led to complex issues with copyright law that current legislation does not address. Authorship, ownership, originality, fair use, licensing, and liability concerns are becoming significant as AI is increasingly used to create educational content for students, write books, and handle other writing tasks. This paper critically examines the copyright implications of generative AI for academics and researchers. The paper also discusses copyright and other IP infringement risks arising from AI-assisted content creation. Emerging judicial decisions, policy initiatives, and legal frameworks adopted across various jurisdictions have also been analyzed. More specifically, the study focuses on educators, researchers, students, publishers, and academic institutions' challenges in managing the balance between technological innovation and the protection of copyright. Using a doctrinal and comparative legal methodology, the paper evaluates whether today's copyright regimes are sufficient to deal with AI-driven creativity and content. The paper provides policy recommendations to enhance legal certainty, promote ethical AI use, and ensure sustainable coexistence between copyright protection and technological advancement in the digital age.
Drivers, multidimensional impacts, and policy implications
Viktor Radun
The growing integration of Artificial Intelligence (AI) into economic and social structures has triggered a global wave of resistance that is increasingly shaping the direction of technological change. This paper offers a multidisciplinary examination of the origins and consequences of AI resistance, drawing on economic, ethical, environmental, cultural and social aspects. Key sources of opposition identified include labor displacement, data privacy violations, ecological costs, cultural frictions, ethical issues, lack of institutional support and improper or insufficient regulations. This study frames the AI resistance as a sophisticated adaptive response to perceived threats and unmet societal expectations. The paper proposes a global AI resistance ecosystem model linking resistance triggers to their economic, social, ethical, and environmental repercussions, while also exploring policy strategies that reconcile innovation with responsible oversight. By interpreting resistance as a legitimate and informative force within democratic governance, this study argues that the central question is not whether resistance will shape AI, but how its insights can be constructively integrated into more efficient, strategic, sustainable, ethical, pro-human and socially accountable AI use in future, to the benefit of all community.
Jesubukade Emmanuel Ajakaye1; Jide Ayoola2
The rapid advancement of artificial intelligence (AI), particularly generative and agentic AI systems, is transforming work practices, professional roles, and employment patterns across multiple sectors. While existing studies have extensively examined the implications of AI for job displacement, augmentation, and workforce restructuring, considerably less attention has been paid to whether higher education institutions are adapting sufficiently to prepare graduates for an increasingly AI-driven labour market. This gap raises important questions regarding the alignment between university curricula, emerging workplace demands, and graduate employability. This paper synthesises current evidence on the relationship between AI-driven labour market transformation and higher education preparedness. Drawing on 32 peer-reviewed studies identified through a PRISMA-guided search process, the review examines how AI is reshaping occupational tasks, the skills and competencies increasingly valued by employers, and the extent to which universities are responding through curriculum redesign, pedagogical innovation, and employability initiatives. Particular attention is given to the transition from occupation-specific training towards the development of transferable capabilities such as AI literacy, critical thinking, adaptability, problem-solving, and human-AI collaboration. The findings reveals a structural misalignment between the pace of AI-driven workplace transformation and the speed of institutional adaptation in higher education. Drawing on evidence from the reviewed literature, the study presents a five-stage conceptual framework linking AI advancement, workplace transformation, evolving skill requirements, university adaptation, and graduate employability outcomes. The framework provides a structured lens for assessing institutional readiness in the age of AI and identifying areas of misalignment between educational provision and labour market needs. The paper concludes with recommendations for universities, policymakers, and industry stakeholders seeking to prepare future graduates for evolving professional environments.
A multiple-case narrative inquiry into AI-assisted work, creativity, ethical boundaries, and educational implications
Tri Pujiani1; Zohaib Hassan Sain2; Cashandra Candice Mara3
Generative artificial intelligence (GenAI) is reshaping how people complete professional, creative, and language-mediated work. While AI is often celebrated for productivity, less is known about how users maintain human judgment, originality, quality, and ethical responsibility when AI becomes part of everyday practice. The purpose of this study was to investigate the ways in which AI is used in different fields, how users manage the use of AI technology in their creativity and ethics, and what implications of such experience can be drawn for education, including language education. The qualitative multiple-case narrative inquiry method was used, involving five participants working in the field of academic activity, entrepreneurship, creation of an e-book, professional translation, social media content production, and book writing for educational purposes. The data were collected via semi-structured interviews and analyzed using thematic narrative analysis. The results reveal that AI served as a partner in productivity, a producer of the first drafts, a partner in thinking, a language improver, and as a facilitator of the workflow. However, AI output was not accepted by participants without any criticism. Users revised, checked, contextualized, and humanized the output of AI in order to retain originality, precision, professionalism, and ethical responsibility. The conclusion of the study is that the use of AI technology is connected with competences, as well as linguistic, critical, creative, and ethical judgment. It implies that education should prepare learners with AI literacy, prompt literacy, critical reading, editing, fact-checking, translation awareness, digital communication, multimodal content creation, and ethical authorship so future generations can use AI responsibly in academic, professional, and creative contexts.
Selmi Djihane1
The integration of artificial intelligence technologies into educational systems has introduced profound governance challenges that demand careful ethical scrutiny and institutional preparedness. Algeria presents a particularly instructive case , a country navigating the pressures of digital modernization in education while working to align its national development ambitions with the growing imperatives of responsible AI deployment at the academic and pedagogical levels. This paper examines the state of AI governance within the Algerian educational context through a critical theoretical lens, analyzing the evolving relationship between the country's national digitalization strategy in education and the pressing need for a coherent, rights-based framework to guide AI integration across learning institutions. Drawing on institutional theory, the study explores how Algeria is positioned to develop regulatory and ethical approaches that reflect its specific educational realities, linguistic diversity, and pedagogical traditions. The paper argues that meaningful AI governance in Algerian education requires building locally grounded ethical frameworks that center student and teacher accountability, safeguard cultural and linguistic identity in learning environments, and ensure equitable access to AI-driven educational tools. This case enriches broader scholarly debates on AI governance across the Global South, offering the Algerian educational context as a meaningful lens through which universal ethical principles can be thoughtfully adapted to diverse national realities positioning Algeria as an active and sovereign contributor to global conversations on ethical AI in education .
A comparative analysis of BRICS and EU regulatory approaches
Sudeshna Sarkar1
This study examines how divergent artificial intelligence governance frameworks in BRICS nations and the European Union reflect competing economic interests and explores their implications for international trade, investment flows, and multilateral AI governance. The EU has established a comprehensive, rights-based, risk-stratified regulatory model centered on the AI Act, emphasizing trustworthiness, fundamental rights, and supranational coordination . In contrast, BRICS countries exhibit fragmented national approaches with limited group-level policy harmonization, prioritizing digital sovereignty, data localization, and state-led development strategies. This regulatory divergence is driven by distinct economic development models, geopolitical positioning, and competing visions of technological autonomy.The analysis reveals critical gaps. BRICS is predominantly treated as a juridical or political grouping rather than an economic policy bloc, and rigorous macroeconomic impact studies comparing regulatory regimes are absent. The paper synthesizes governance-focused and international relations studies to map the contours of this regulatory fragmentation, identify its economic drivers and consequences, and propose pathways toward more coherent multilateral AI governance that balances innovation, sovereignty, and equitable development.
Artificial Intelligence as a Catalyst for Economic Connectivity Between the Diaspora and the Home Country
Miodrag Simić1; Radovan Milovankić2; Marko Pavlović3
Contemporary developments in the digital economy increasingly highlight the importance of the diaspora as a valuable economic, business, and communication resource for countries of origin. However, existing mechanisms for connecting diaspora communities with domestic markets often remain fragmented, insufficiently personalized, and largely confined to traditional institutional or promotional channels. Within this context, digital marketing and artificial intelligence (AI) create opportunities for developing hybrid business models that integrate digital platforms, data analytics, automated communication, and human networks of trust.
This paper aims to examine the role of AI-supported hybrid business models in digital marketing as a mechanism for strengthening economic connectivity between diaspora communities and their countries of origin. Particular attention is devoted to exploring how artificial intelligence can facilitate diaspora segmentation, personalized communication, predictive identification of business interests, and more effective engagement among market participants, investors, and institutional stakeholders. The anticipated findings suggest that AI can serve as a catalyst for transforming existing approaches to diaspora engagement, particularly through more precise audience targeting, automated customer support, behavioral analytics, the development of personalized offerings, and the establishment of more sustainable digital business ecosystems. Hybrid business models enable the integration of technological efficiency with the social capital embedded within diaspora networks, thereby enhancing opportunities for investment, exports, tourism, knowledge transfer, philanthropy, and the development of entrepreneurial initiatives. The significance of this study lies in its conceptualization of the diaspora not merely as a demographic or institutional phenomenon but as an active economic segment that can be strategically engaged through digital marketing and AI-driven systems. The implications of the study are relevant to policymakers, chambers of commerce, marketing agencies, investment networking platforms, and businesses seeking to develop more effective strategies for communication, economic engagement, and collaboration with diaspora communities.
Governance challenges in the AI economy
Kamel Dhay1
This research examines the new frontier of entrepreneurship and digital work by Syrians in the context of economic reopening, post-war recovery, and increasing global digital integration. As the world undergoes labour-market transformation driven by artificial intelligence (AI), online labour platforms, and digital business tools, new opportunities are opening up for Syrians to earn money, become self-employed, and start enterprises. At the same time, significant governance challenges remain regarding regulation, infrastructure, labour protections, access to finance, digital inclusion, and ethical use of AI systems. The study asks whether these technologies can reduce traditional barriers to entrepreneurship, including geographical isolation, limited formal employment, and restricted market access, or whether they risk reproducing inequality through algorithmic bias, weak institutional oversight, and unequal digital capabilities. Using a qualitative exploratory approach, the paper draws on 6 semi-structured interviews with Syrian entrepreneurs. This analysis is theoretically grounded in Mixed Embeddedness Theory, which explains entrepreneurship as shaped by individual skills, digital tools, and institutions, showing Syrian digital entrepreneurship as structurally embedded within evolving platforms, AI systems, and uneven governance contexts. The findings, interpreted through Mixed Embeddedness Theory, imply that the Syrian entrepreneurial opportunities developing through the AI economy are co-determined by personal resources (including skills, digital literacy, and networks) and emerging opportunity structures (including AI-facilitated platforms and global digital labour markets) and limited institutional environments (characterised by unequal regulation, infrastructural constraints, and fragmented governance). The benefits of AI-driven digital ecosystems can be seen as potential sources of income and self-employment, but their long-term positive effects depend on the institutional conditions that actively channel those benefits.
Regulation, rights, and the future of work in the age of AI
Ridhima Gupta1
Artificial Intelligence (AI) has emerged as one of the most transformative technological developments of the twenty-first century, reshaping education, employment, public administration, healthcare, communication, economic production, and social relations. The rapid expansion of generative artificial intelligence has intensified debates regarding the appropriate relationship between technological innovation, democratic governance, fundamental rights, and the future of work. Although AI has the potential to improve productivity, expand access to knowledge, support personalised education, assist scientific discovery, and create new forms of employment, its deployment also produces significant risks relating to privacy, discrimination, surveillance, misinformation, labour displacement, algorithmic opacity, intellectual property, and unequal access to technological benefits. These challenges demonstrate that AI governance cannot be treated merely as a technical or commercial issue. It is fundamentally a question of law, rights, social justice, institutional accountability, and democratic control. This paper examines the governance of artificial intelligence through a systematic and thematic review of existing academic literature, international policy frameworks, regulatory developments, and institutional reports. The study focuses primarily on four interconnected dimensions: governance and regulation; fundamental rights and social justice; education and knowledge; and work and labour transformation. The analysis demonstrates that existing AI governance approaches are increasingly moving from voluntary ethical principles towards more structured, risk-based and rights-oriented regulatory frameworks. However, significant gaps remain in enforcement, global coordination, accountability, transparency, and protection for vulnerable communities. The European Union's risk-based regulatory approach, UNESCO's human-centred guidance on generative AI in education and research, the NIST AI Risk Management Framework, and the United Nations' emerging proposals for global AI governance illustrate the diversity of contemporary regulatory responses. The paper argues that effective AI governance requires a multi-level and human-centred framework based on accountability, transparency, privacy, non-discrimination, human oversight, labour protection, democratic participation, and equitable access. In particular, AI regulation must move beyond the narrow objective of preventing technological harm and address the structural distribution of power, wealth, data, and decision-making authority. The future of AI governance should therefore be understood not as a choice between innovation and regulation, but as a process of ensuring that innovation remains compatible with human dignity, fundamental rights, social equality, democratic institutions, and decent work.
Rania Ahmad1; Subrato Kumar Dey1
Despite the significant transformation that artificial intelligence has brought about in the paths and mechanisms of developing many different businesses and economic activities, knowing the type, size, and directions of transformation that artificial intelligence brings about in labor markets in the context of small and medium enterprises adopting this knowledge revolution still needs more study and analysis. This study explores the reality and impact of the use of artificial intelligence (AI) in a sample of small and medium enterprises (SMEs) in the Syrian Arab Republic, through a set of indicators that illustrate the transformation in the number and type of workers, as well as the transformation in job descriptions and required skills, in addition to exploring the social responsibility of these enterprise owners in relation to their role in qualifying and training workers and raising their capabilities to reduce their dismissal or replacement with new workers . The results of the analysis of data collected using an e-questionnaire indicate that the adoption of AI in Syrian SMEs may still be in its early stages, but some indicators may serve as an early warning of an upcoming transformation, with the expectation of increased adoption in the future, which calls for proactive policies and plans to support the positive transformation in labor markets. The paper presents a set of recommendations that may help decision-makers and experts to develop policies that focus on the development of small and medium enterprises in particular and link them to educational policies that aim to achieve consistency and harmony between the development of educational outputs and the transformation in labor markets brought about by artificial intelligence in these markets.
Lessons from Nigeria's 2018-2023 electoral reforms for Africa
Njoku Angelina Chigozie1
This study examines 'Re-engineering electoral systems for electoral democracy, lessons from Nigeria's 2018 to 2023 electoral reforms for Africa'. The specific objectives of the study are to identify legal and technological shortcomings in Nigeria's elections from 2018 to 2023, to examine how these shortcomings affected public trust in Nigeria's elections, and to draw key lessons for re-engineering electoral democracy in Africa. The issue is that public trust in Nigeria's 2023 election was brought low by poor laws and inadequate technology. Using secondary data from reports, statutes, and journal articles, the study employed a qualitative design. The Technology Acceptance Model and Digital Governance Framework served as a framework for the thematic analysis of the data. The results reveal two primary gaps: first, the Electoral Act 2022 was unclear about uploading IReV results in real-time. Second, IReV failed due to inadequate networks and training. BVAS was successful in voter checks. Trust in INEC dropped to 23% as a result of these flaws. The study concluded that technology cannot foster trust on its own. Early testing, trained personnel, and clear legislation must all cooperate. The most important lessons for Africa are to test all electoral technologies before using them, train personnel early, and adopt clear rules.
Challenges in attributing liability in light of comparative legal frameworks
Omeir Hadjira1
The rapid development of artificia intelligence (AI) technologies has opened up significant opportunities for economic growth, the promotion of digital transformation and the support of innovation in Algeria. Conversely, the expanding use of artificial intelligence systems in automated processes and decision-making activities has contributed to the emergence of new forms of criminal risks and unlawful behaviour. These developments have raised fundamental legal issues concerning the determination of criminal liability where offences are committed by or with the aid of artificial intelligence systems. This study aims to examine criminal liability for AI-related offences under Algerian law, by analysing the adequacy of the provisions of the Algerian Penal Code in addressing offences involving AI systems, given that traditional rules of criminal liability are based primarily on human intent, personal fault and the principle of individual liability. The study also addresses the legal issues arising from the intrinsic and evolving characteristics of advanced artificial intelligence systems, particularly with regard to determining liability amongst programmers, developers, manufacturers, operators and users. The study adopted an analytical legal approach to assess the suitability of current criminal law rules in addressing emerging crimes linked to artificial intelligence, It also highlighted the legislative challenges posed by technological developments and emphasised the need to adapt the legal framework to ensure the establishment of effective criminal liability, whilst at the same time continuing to encourage technological innovation and support the digital transformation process. The study concludes that the existing Algerian legal framework remains insufficient to address all the legal issues arising from crimes linked to artificial intelligence, which necessitates legislative reforms aimed at clarifying the rules for attributing criminal liability, enhancing legal certainty, and providing effective protection against the risks arising from increasingly autonomous and complex artificial intelligence systems.
Implications for education, labour, and society
Rutvi Meena1; Asit Balwantrai Acharya2
Artificial Intelligence (AI) is no longer just a technological innovation; it has become a powerful force reshaping governance system, human rights frameworks, and the future of work across societies. Its growing use in education, labour markets, and public administration offers significant opportunities for efficiency, personalization, and innovation. At the same time, it raises serious concerns regarding accountability, transparency, fairness, and the protection of human dignity. This paper examines the evolving landscape of AI governance, focusing on how regulatory frameworks attempt to balance rapid technological advancement with ethical responsibility. It highlights key challenges such as algorithmic bias, risks to data privacy, and the need to safeguard digital human rights. Addressing these challenges requires strong oversight mechanisms and inclusive, participatory policymaking that reflects diverse social perspectives. In the context of education, the integration of AI into universities is transforming teaching, learning, and assessment practices, demanding new governance approaches to ensure fairness and academic integrity. In the labour sector, the paper explores the socio-economic impact of automation, the expansion of the gig economy, and the urgent need for reskilling and upskilling to prevent widening inequalities. Adopting an interdisciplinary perspective, the study situates AI within broader social and cultural contexts, drawing on insights from law, ethics, sociology, and technology studies. It argues for a model of responsible AI governance that prioritizes human well-being, protects labour rights, and promotes inclusive and equitable development. Ultimately, the paper contends that effective governance is essential to ensure that AI serves as a tool for social progress rather than a source of exclusion and disparity.
Samet Kurtağzı1
Since the emergence of Bitcoin in 2009, crypto assets have proliferated globally as a decentralised and cross-border hybrid of technology and finance, transforming the traditional financial intermediation model. Türkiye stands out as one of the leading countries worldwide in crypto asset usage, owing to its young and techsavvy population, high inflation environment, and exchange rate volatility. For a long time, Turkish law lacked a clear definition and comprehensive regulatory framework for crypto assets; under Law No. 6493, only their use as a payment instrument was prohibited, while trading, custody and taxation remained largely unregulated. This situation changed radically with Law No. 7518 on the Amendment of the Capital Markets Law, which entered into force on 2 July 2024, bringing crypto asset service providers under the licensing, supervision and sanctioning authority of the Capital Markets Board (CMB). Adopting a descriptiveanalytical method and a comparative law perspective, this study examines the current state of crypto asset law and tax law in Türkiye in light of Law No. 7518, the CMB's Communiqués III35/B.1 and III35/B.2, the December 2024 amendments to the MASAK regulations, and the crypto asset transaction tax and withholding tax regime adopted in early 2026. By comparing Türkiye's regulatory model with those of Japan, the United States, Switzerland and the European Union (MiCA), the study finds that the current framework has achieved significant maturity in supervision and licensing, while still containing gaps in tax legislation, investor protection mechanisms and judicial precedent. The conclusion offers concrete recommendations for legal practitioners and policymakers.
Cicih Ratnasih1; Syaiful1; Mohammad Rizal Tamami1; Eka Tofa Kumalasari1
This study aims to analyze the effect of training and technology usage ability on employee performance, and its impact on increasing compensation for Contact Center agents at Bank Permata, Jakarta. Targeted training is expected to enhance work competencies, while technological mastery is a key requirement to support the effectiveness of call center services. Both factors are believed to contribute to achieving more optimal performance, which in turn affects increased compensation as a form of company recognition of employees.
Insights from diverse professional sectors
Vesna Radojcic1; Pavle Dakic2; Sasa Ljubojevic3; Nebojsa Todorovic4
The growth of artificial intelligence (AI) and automation is a major factor changing the global labor market. It greatly affects job structures and the shift in traditional roles. This paper looks at how AI systems are causing certain jobs to disappear while also pointing out new types of work that need specific skills. In addition to reviewing relevant literature, we surveyed 40 people from various educational and professional backgrounds. The goal was to understand their views on how AI affects the labor market, what skills are now needed, and the importance of ongoing professional development. Most respondents think that AI will greatly change current jobs, but it will also create new roles. They stress the importance of further education and developing digital skills. Both the literature review and survey results highlight the need for ongoing education, reskilling, and careful management of technology changes to maintain human talent. The study concludes that we should not see AI just as a threat to jobs. Instead, with the right policies, planning, and human oversight, it can help foster long-term economic and social growth.
Nur Çelik Efşan1
It is known that technological developments have influenced interstate relations from the past to the present. While the development of transportation routes formed the basis of diplomatic relations in traditional diplomacy, in the 21st century, with the development of studies on artificial intelligence, digitalization activities in the private sector have spread to the public sector, and artificial intelligence dominance is observed in a wide range of areas in diplomacy, from intelligence gathering and increased security measures to electronic service delivery and the preparation of international political and commercial texts using special software. All of these have paved the way for the digitalization of diplomacy. Indeed, digital diplomacy is a foreign policy tool through which a country maintains its connections with other states through negotiations and mutual cooperation without resorting to hard power. The announcement of digital diplomacy to the public through the new media order has increased transparency, and with the removal of communication barriers due to the impact of globalization and the widespread use of internet technologies in state administration, it has taken its place as a tool for influencing and informing individuals. Digital diplomacy; While facilitating the conduct of diplomatic relations online, it is also a supporting factor that enables states to maintain a strong image in the international arena. The aim and importance of this study is to analyze the advancement of diplomatic relations on the basis of artificial intelligence in our century shaped by artificial intelligence. A literature review was conducted in the study
Ethical challenges and social responsibility in the transformation of work, culture, and human relations
Nikhil Thakur1; Syed Asghar Mehdi2; Shilpi Bhatia3
Background: The hospitality industry has seen the rise of artificial intelligence adoption and at the same time, bleisure tourism has become a widely popular segment of travel. These two trends are changing the way services are delivered, however, they are also raising ethical issues such as privacy, employment, and human interaction. Objective: This paper is aimed at investigating the ethical dilemmas and social responsibility aspects of the use of artificial intelligence in bleisure tourism and hospitality, especially in terms of changes in work, culture, and human interaction. Methodology: A systematic review of the literature was carried out using the databases Scopus, Web of Science, Emerald Insight, ScienceDirect, and Google Scholar. The focus was on peer-reviewed articles published from 2014 to 2025. A thematic content analysis was performed. Results: AI is capable of significantly improving the efficacy of operations and the level of personalization for bleisure tourists. Nevertheless, the issues of data security, discrimination by algorithms, loss of jobs for employees, monitoring of employees, and the diminishing of genuine human contact have all been highlighted as problems. Conclusion: In order to incorporate artificial intelligence into bleisure tourism and hospitality in a way that is sustainable, it is crucial to find a point of equilibrium between technological advancements on the one hand and the ethics of responsibility, the values of human-centered service, and social sustainability over the long term on the other.
AI, labour rights, and professional ethics
Ashok K. Rathoure1
The inclusion of artificial intelligence in schools is quickly changing how we teach, manage, and support students. While AI offers improved efficiency, personalization, and access, it also raises significant issues regarding workers' rights, professional independence, and ethical practices in education. This paper looks closely at the growing conflicts between AI-driven automation and the human-centered values that have long defined educational work. Drawing on insights from labour law, educational ethics, and technology studies, the analysis looks at three main aspects of AI's effect on educational work. First, it examines the loss of teacher autonomy due to management by algorithms and surveillance systems. Second, it discusses how AI could reduce the skills needed by educators while shifting professional responsibilities to automated platforms. Third, it highlights the failure of current legal frameworks to deal with issues like liability, consent, and collective bargaining rights in AI-driven educational settings. This article argues that without active governance, AI could worsen unstable working conditions, lower professional judgment, and weaken the important relationships in teaching. On the other hand, if AI is developed with input from educators and based on principles of rights, it can enhance professional practice instead of replacing it. The article ends with practical suggestions for policy-makers, institutional leaders, and teacher unions to ensure that the future of work in education is driven by ethical awareness, social discussions, and a strong dedication to labour rights.
Strengthening corporate ethical accountability in educational technology enterprises
Alok Kumar1
Artificial Intelligence (AI) has become the backbone of the educational‑technology (EdTech) ecosystem, transforming how content is delivered, assessed, and tailored to learners. Yet, algorithmic opacity, data bias, and the ecological costs of intensive computation raise ethical and sustainability concerns. Investors and regulators increasingly demand transparent Environmental, Social and Governance (ESG) reporting that accounts not only for carbon and labor metrics but for digital integrity. Drawing on stakeholder and stewardship theories, this research investigates how AI‑governance maturity influences ESG‑reporting quality and corporate ethical accountability in global EdTech enterprises. Employing a mixed‑methods design (quantitative survey n = 60 firms; qualitative interviews n = 15), regression and mediation models reveal a significant positive effect of AI‑governance mechanisms - ethics boards, algorithmic audits, and bias‑control training—on ESG disclosure comprehensiveness (β = 0.54, p < 0.001). Ethical accountability partially mediates this relationship, transforming compliance into stakeholder trust. Theoretically, the paper contributes a construct of Algorithmic Accountability as Governance Capital (AAGC) linking digital responsibility with sustainability performance. Managerially, it proposes integrating AI metrics within ESG dashboards, dual financial/AI audit certifications, and sector‑specific reporting guidelines. These findings advance understanding of responsible innovation and position EdTechs as pioneers of transparent, ethics‑centric digital sustainability.
The Relationship with Perceived Decision-Making Autonomy and Responsibility
Bojana Nešić1
This study examines how the use of and reliance on artificial intelligence in employee selection processes affect HR professionals’ and candidates’ perceptions of decision-making autonomy and responsibility. The aim is to investigate how greater use of AI changes individuals’ sense of personal control and responsibility in decision-making, as well as to examine whether these effects differ between the two groups. Existing research has focused primarily on the efficiency, acceptance, and ethical frameworks surrounding the use of artificial intelligence in HR processes. However, the psychological consequences of AI use, particularly in terms of perceived decision-making autonomy and responsibility, remain insufficiently explored. Moreover, differences in these outcomes between HR professionals and candidates have received relatively little research attention. The study will employ a survey questionnaire to assess the use of and reliance on artificial intelligence and perceptions of control over decision-making. The constructs are based on the Trust in Automation Scale (Jian et al., 2000; Körber, 2019) and perceived behavioral control (Ajzen, 1991). It is hypothesised that greater use of and reliance on artificial intelligence will be associated with a reduced sense of control over decision-making and a partial shift of responsibility from the individual to the technological system. These effects are expected to differ between HR professionals and candidates, contributing to a better understanding of behavioural patterns associated with the use of artificial intelligence in employee selection.
“When Free Isn’t Free”: Biometric Signatures and Mechanisms of Manipulation in the Digital Ecosystem
Aleksandra J Dimitrijević1
Contemporary digital marketing has moved beyond traditional methods of user tracking. This review paper synthesises insights from digital communication studies, cognitive framing, and data science. It focuses on the collection of metadata that is not readily apparent to the average user, not through direct audiovisual surveillance but through subtle forms of sensor-generated data. The paper examines the role of accelerometers, gyroscopes, and users’ biometric signatures in the creation of detailed psychological profiles. Drawing on Shoshana Zuboff’s theoretical framework of surveillance capitalism, it explores how algorithms use physiological parameters derived from users’ interactions with their devices to predict emotional states. The mechanisms underlying personalised advertising can be compared to aggressive telephone sales techniques, but may achieve greater success because they allow for automated targeting of individuals’ cognitive vulnerabilities. The ultimate aim extends beyond selling products. The continuous measurement of user behaviour through the Android system provides data for deep learning, whereby the user ceases to be merely a customer and becomes a source of raw material for the development of artificial intelligence. The paper highlights the ethical challenges of an era in which privacy is exchanged for the appearance of free access to digital services, emphasising the need for critical digital literacy.
Generation x's perceptions of SGFinDex in Singapore's retirement planning ecosystem
Daniel Tan1
Singapore's demographic landscape is increasingly defined by the dual pressures of an accelerating ageing population and a critically low fertility rate, necessitating a strategic shift toward individualised retirement adequacy. Within the "Smart Nation" framework, the Singapore Financial Data Exchange (SGFinDex) was established as the world's first foundational public digital infrastructure to resolve data fragmentation for the "sandwich generation" (Generation X). This qualitative study examines the perceptions of this cohort (born 1965–1980) through three semi-structured focus groups and reflexive thematic analysis. The findings reveal five core themes: Visibility Without Guidance identifies a structural lacuna between data consolidation and the "algorithmic production of meaning" necessary for actionable retirement insights; Trust as Context suggests that while high institutional trust serves as a baseline, it remains decoupled from the perceived security of downstream data usage by private entities; Usability Friction highlights barriers in re-linking requirements and mobile interface habits; Fragmentation Anxiety illustrates how partial data integration creates a significant "cognitive burden"; and Advisor Mediation positions human intermediaries as essential "adoption multipliers." These results expand the Technology Acceptance Model (TAM) by critiquing "pure" digital-only governance models. As the MyMoneySense application is decommissioned, this research advocates for the integration of Human-AI hybrid models and Explainable AI (XAI) to provide the interpretive guidance currently missing. By addressing the intersection of social responsibility and digital governance, the study offers a critical perspective on how public trust in technological solutions is maintained through mediated interaction within complex socio-technical systems.
Junaid Kc1; Divya Nv2
AI is increasingly becoming part of the education and business value chain, and this is changing the scope and nature of entrepreneurship training and competency building. While traditional entrepreneurship education has focused on creativity, innovation, problem-solving, leadership, and opportunity recognition, the advent of AI-powered technologies is reshaping the methods and implications of teaching, learning, and practice in modern digital economies. This paper critically analyses the contribution of AI in revolutionising the definition of entrepreneurial skill development in entrepreneurship training and management education. This study examines how AI-powered business planning tools, intelligent mentoring systems, predictive analytics, simulation platforms, and generative AI applications are utilised in entrepreneurial learning settings. It also examines the role of AI technologies in training models to foster digital adaptability, strategic thinking, innovation capacity, data literacy, and decision-making skills among emerging entrepreneurs. Although AI holds promise for customised learning, improved market analysis, and faster idea generation, it also introduces new ethical and technological concerns, such as the risk of a dependence on AI, a decline in human creativity, and unequal access to AI tools. Based on current interdisciplinary literature, the paper emphasises the importance of integrating AI complementary, human-centric, and ethically responsible within entrepreneurship training systems. The research adds to the academic debate on the direction of entrepreneurship education and highlights the need to foster adaptive and resilient entrepreneurial skills in the era of AI.
A review of evidence from developing economies
Anjan Niyogi1; Pallavi Kumari2
The rise of Artificial Intelligence in higher education is transforming learning, student assessment, academic management, and institutional strategies in diverse global contexts. In this paper, the transformative impact of AI-driven teaching technologies on enriching personalised learning, adaptive assessment, predictive analytics, and evidence-based decision-making at universities. Motivated by the Technology Acceptance Model and institutional change theory, this study investigates the role of digital readiness, faculty acceptance, policy orientation, and socio-cultural background that will affect AI adoption in academic ecosystems (Davis, 1989; Zawacki-Richter et al., 2019). Using a comparative analysis of emerging scholarship and policy environments, the paper examines institutional experiences across Canada, the European Union, the Balkan Peninsula, MENA, and representative countries of the Global South. The comparative findings suggest that universities in Canada and the European Union are well equipped with AI governance frameworks, as well as having strong digital platforms, regulatory environments, and strategic investment. Meanwhile, organizations in the Balkans, MENA, and the Global South are innovative, even as they grapple with infrastructure, ethical, and policy obstacles. The study identifies, among other things, continuing concerns about algorithmic bias, academic integrity, data privacy, and the maintenance of pedagogical autonomy (Selwyn, 2019). Sustainable institutional reform, it contends, requires human-centered, ethically governed, and contextually respecting AI-embedded implementation approaches that can ensure a well-rounded balanced approach between technological advancement and educational equity, cultural diversity, and long-term academic resilience.
Cheryl Cheng Xin Yi1
Generative artificial intelligence (GenAI) has emerged as a rapidly developing technology with significant potential to enhance the effectiveness and quality of managerial decision-making. However, there are many potential concerns associated with the impact of GenAI outputs on managerial trust. These include the lack of transparency, data bias, errors, inaccuracies, and inconsistencies. This study aims to investigate the evolution of managerial trust in GenAI outputs and its impact on decision-making across the financial institutions. It draws on theories from Trust-in-automation, technology adoption, human–AI collaboration and organizational governance. This study then constructs a conceptual framework and empirically tests the relationships among the GenAI adoption, evaluation capabilities, internal control, and verification, and trust in GenAI outputs. Meanwhile, it explores the mediating role of trust between GenAI adoption and managerial perception of effective decision-making. An online survey was distributed to obtain the primary data for this study. A total of 100 valid responses were obtained and analyzed using SmartPLS4 to examine the structural relationships among the constructs. The results revealed that frequent usage has a significant positive impact on trust towards GenAI outputs. It led to higher confidence among managers in the decisions made. Furthermore, trust in GenAI outputs acts as a partial mediator between usage and decision-making effectiveness. The findings suggest that trust is formed more directly through experiential use of GenAI than through governance structures. The study identified actions that organizations can take to ensure ethical practice of GenAI-assisted decision-making.
José G. Vargas-Hernández1; Patricia Villasana-Ramos2; Omar C. Vargas-González3
The intersection of artificial intelligence (AI) and green finance represents one of the most consequential developments in Mexico's transition toward a low-carbon economy. This research report examines how AI-driven technologies—including machine learning, natural language processing, deep neural networks, and predictive analytics—are reshaping the landscape of renewable energy finance in Mexico. The findings contribute to an emerging body of knowledge that positions Mexico as a middle-income country navigating both the opportunities and the structural constraints associated with AI-enabled green finance. Policy recommendations emphasize the need for open data ecosystems, AI governance frameworks aligned with national energy transition goals, and public-private investment in digital literacy and climate fintech capacity.
Opportunities, ethical challenges, and future implications
Shivansu Sachan1
Artificial Intelligence (AI) is transforming the global hospitality industry by redefining customer interaction, service personalization, operational efficiency, and guest satisfaction. This paper examines the role of AI-driven technologies such as chatbots, virtual assistants, predictive analytics, facial recognition systems, and smart hotel management platforms in enhancing guest experiences and building long-term customer loyalty. The study further explores how AI contributes to personalized services, faster response systems, automated decision-making, and improved service quality in hotels and tourism establishments. At the same time, the paper critically analyzes the ethical and social challenges associated with AI adoption in hospitality, including concerns related to data privacy, surveillance, employment displacement, algorithmic bias, and reduced human interaction. Through interdisciplinary perspectives combining tourism studies, management, technology, and ethics, the research highlights the need for responsible and human-centered AI implementation. The paper also discusses the future implications of AI for hospitality education, workforce development, and sustainable tourism practices. It argues that while AI can significantly improve operational effectiveness and customer satisfaction, maintaining emotional intelligence, cultural sensitivity, and human values remains essential in hospitality services. The study concludes that a balanced integration of technological innovation and human interaction is necessary for creating ethical, inclusive, and sustainable guest experiences in the age of artificial intelligence.
A comparative study on Singapore's regulatory sandbox with the Indian regulatory mechanism
Nirankush Kenjige1; Chaitra Rangappa Beerannavar2
A 'regulatory sandbox' typically involves testing of new products or services in a controlled, regulatory environment. This helps to gather evidence on the benefits and risks of a new financial innovation, while monitoring and managing the risks. The sandbox provides a structured way for regulators and the innovators, to interact with the ecosystem, and assist the regulators to develop regulations that encourage innovation and enable the delivery of relevant, low-cost financial products. It is an important tool that can create a more dynamic, evidence-based regulatory environment that helps to adapt the various emerging technologies. The paper devolves around the niche 'regulatory sandbox' mechanism as followed by Singapore, alongside a comparative analytical study with jurisdiction of India, and India's stance on the regulatory mechanism. The paper aims at analysing the regulatory fallacies in the Indian system by analysing the risks involved, and thereafter looking at the security and financial implications. Ever since the increased valuation and societal consideration of the block chain technology and the secured nodal transactions, the impact and importance it gained, in order to surface a regulatory sandbox in Singapore - being a notable and imminent participant in the technological advancements attached to the financial technology- is inevitable. From P2P lending to the perils of Insurtech, Singapore has been evolving towards the creation of a regulatory sandbox for the fintech industry and hence the understanding of the same plays a pivotal role in the overall Performa. Start-ups such as ADDX and BondEvolve are paving the way to provision of direct access to the financial market and thereby, assisting in the general mitigation in Singapore. Hence, the progress, and risk factors attached with it, require in-depth detailing and introspection
Nagendra Singh Yadav1; Vishal Kumar Goar2; Pallavi Singh Yadav3
This paper examines how the introduction of LLMs (Large Language Models) into commercial software is driving a structural transformation of work, management practices and economic value capture across the software industry, a transformation with consequences for professions, institutional adaptation and digital labour markets. For nearly two decades, SaaS (Software as a service) business models rested on a single load-bearing assumption that the marginal cost of serving an additional user was effectively zero. This assumption justified flat rate subscriptions, i.e. all you can eat pricing tiers and the 75-85 percent gross margins that defined cloud software economics through the 2010s. The integration of LLMs (Large Language Models) into commercial software products has broken that assumption. Every model inference now consumes real, metered compute and the cost scales with prompt length, response length, model size and request frequency. We document this structural shift, develop a quantitative framework, the inference Cost Propagation Framework for understanding how token-level prices set by foundation model providers cascade through application layer business models and analyze the strategic responses now visible in industry data. Drawing on May 2026 pricing from OpenAI and Anthropic, public company disclosures and the ICONIQ Capital 2026 State of AI survey, we show that average AI product gross margins have settled near 52 percent against a SaaS benchmark of 75-85 percent, with inference alone consuming roughly 23 percent of revenue at scaling stage firms. The paper closes by identifying which firms, professions and institutional arrangements are most exposed to these new economics and what governance and management responses are emerging
Poornima Mahesh1
This paper proposes a rights-based regulatory design to address harms caused by AI systems in the workplace, centering remedies and redress mechanisms that protect workers' autonomy, privacy, and livelihoods. centring remedies and redress mechanisms that protect worker autonomy, privacy, and livelihood. The Objectives are threefold: (1) map the main categories of AI-related workplace harms (surveillance, automated decision-making in hiring, task allocation and performance evaluation, and opaque disciplinary algorithms); (2) assess existing legal and institutional remedies across jurisdictions with emphasis on gaps in applicability; and (3) propose a workable regulatory architecture that integrates statutory rights, accessible remedies, and organizational accountability measures. to map the principal categories of AI-related workplace harms (surveillance, automated decision-making in hiring, task allocation and performance evaluation, and opaque disciplinary algorithms); (2) to evaluate existing legal and institutional remedies across jurisdictions with emphasis on enforceability gaps; and (3) to propose a practicable regulatory architecture that integrates statutory rights, accessible redress pathways, and organizational accountability measures. The Methodology combines doctrinal legal analysis, comparative policy review (India, EU and select Global South contexts) and qualitative case synthesis of recent litigation and regulatory actions involving platform and corporate employers. EU, and selected Global South contexts), and qualitative case synthesis of recent litigation and regulatory actions involving platform and enterprise employers. The analysis highlights recurring obstacles – burden of proof, opacity of evidence, regulatory fragmentation and power asymmetries between workers and employers – and examines tools such as algorithmic impact assessments, mandatory explainability standards, collective bargaining supplements and specialized ombudsman or court processes. obstacles—burdens of proof, evidentiary opacity, regulatory fragmentation, and power asymmetries between workers and employers—and examines tools such as algorithmic impact assessments, mandated explainability standards, collective bargaining complements, and specialized ombuds or tribunal processes. Expected contributions include a modular regulatory model that links the articulation of rights to procedural remedies, the design of principles for enforceable remedies (including presumptions of transparency and evolving burdens of proof), and policy recommendations tailored to jurisdictions with limited enforcement capacity. articulation to procedural remedies, design principles for enforceable redress (including presumptions of transparency and shifting evidentiary burdens), and policy recommendations tailored for jurisdictions with limited enforcement capacity. The paper is aimed at policymakers, labor regulators and academics who are looking for concrete frameworks to ensure that the use of AI at work respects fundamental rights and provides rapid and effective remedies. targets policymakers, labour regulators, and scholars seeking actionable frameworks to ensure AI use at work respects fundamental rights and provides timely, effective remedies.
Felipa Lopes Dos Reis1; Bernardo1; Afonso1; Rui Castaneira1
This research sets out to explore and compare how quality management is perceived and implemented in two Worten retail locations — specifically, the store in Rio Sul and the store in Colombo. The main goal is to analyze the sales sector through the eyes of the customer, particularly focusing on how feedback provided by consumers can influence the adaptation or improvement of a particular quality dimension within these environments. To carry out this comparative analysis effectively, the research adopts a methodology based on the SERVQUAL model — a widely recognized tool in the domain of quality management. This model facilitates the evaluation of service quality by examining the gap between customers' expectations and their actual perceptions of the service received. By working through this gap, researchers can understand not only how well a service is being delivered but also how closely it aligns with what customers value. The SERVQUAL framework comprises five main dimensions that provide a comprehensive view of service quality: tangibility (which relates to the physical appearance of facilities, equipment, and staff), reliability (referring to the ability to perform the promised service accurately and dependably), responsiveness (which captures how willing and prompt employees are in providing service), assurance (the knowledge and courtesy of staff, as well as their ability to instill trust), and empathy (the extent to which personalized attention and care are offered to customers). A structured questionnaire, grounded in these five dimensions, was designed and distributed to customers at both Worten stores. The responses gathered from this questionnaire form the empirical base of the study and are analyzed to identify strengths and weaknesses in service quality at both locations. By interpreting these data, the research aims to offer a detailed and data-informed comparison, shedding light on how service quality is experienced differently in two branches of the same retail chain. Ultimately, this study seeks not only to assess the current state of service quality management at the Rio Sul and Colombo Worten stores but also to identify actionable recommendations that could enhance customer satisfaction. The research contributes valuable insights for management teams looking to refine their approach and tailor their services more effectively to customer expectations, particularly in a highly competitive and dynamic retail sector.
Tarana Afrin Chandel1
Conventional teaching methodologies are rapidly being replaced by digital-tech education with flexible and personalized learning. These learning options have opened the doors to micro-credentials, enhancing the employability rates of students. Universities and higher education institutes (HEIs) have started taking this into consideration. Aiming to enhance employability by strengthening industry-academia collaboration, this study investigates how these two parameters affect learner outcomes and global employability. Covering the period from 1990 to 2025, the study analyzes how significant skills gaps, conventional practices, and static higher education degrees hinder long-term career success. This study examines the influence of AI-related micro-credentials on the employment designations for students in higher education. A quantitative correlational design was utilized to examine the relationships among the AI-Driven Skill Model (AIS), Job Market Transformation (JMT), Micro-Credentialing System (MCS), AI-Driven Curriculum Redesign (ACR), and Global Employability (GE). Data indicates a substantial positive correlation between the acquisition of micro-credentials and employability. The data demonstrates that micro-credentials serve as clear indicators of job readiness, linking educational programs with workforce requirements. This highlights the significance of incorporating micro-credential programs into bachelor's curricula to improve graduate job prospects in the digital age.
Revisiting the j-curve hypothesis
Riddhi Mehta1; Arshad Bhat2
The J-curve hypothesis has been used in long time to analyze the relationship between the exchange rate fluctuations and adjustment in trade balance especially in developing economies. This paper re-tests the existence of the bilateral J-curve effect in the case of India in monthly data and in a multivariate time-series model that both accounts for the short-run dynamics and the long-run relationships. The evaluation is based on the bilateral trade of India, with the 5 top trading partners, namely the United States, China, Japan, Germany, and the United Arab Emirates during the years 1991-2024. Unit-root tests, Johansen cointegration tests and Vector Error Correction Models are utilized where they are statistically and diagnostically appropriate. The findings suggest that bilateral trade relations of India do not depict a strong/systematic J-curve effect. There is also long-run cointegration of the limited number of trading partners and no long-run consistent relationship between the real exchange rate and the trade balance of China and Japan. Even though cointegration is found to be the case in Germany, the instability of the parameters is indeed high, due to probable macroeconomic and structural shocks within the Eurozone, which restricts the accuracy of the dynamic VECM estimation. In the case of the United Arab Emirates and the United States, there are weak and statistically insignificant adjustment dynamics at the error-correction mechanisms and impulse response analyses after the exchange rate shocks. Altogether, the results indicate that the adjustment mechanisms, based on exchange rates, have a minor impact on the bilateral balance of the Indian trade, and the structural factors prevail in the dynamics of trade. These findings have significant implications to the exchange rate management and other external sector policy in India.
Emerging challenges in ownership, creativity, and patent governance
Varanasi Rahu1
Generative Artificial Intelligence (Generative AI) has revolutionised creativity and innovation across fields such as education, business, media, healthcare, and the tech industry, transforming how people think and work. Today, AI tools like ChatGPT, Gemini, Midjourney, and DALL·E can create content independently from humans in all forms: text, images, software code, designs, and inventive solutions, which now pose a threat to traditional notions of authorship, ownership, inventorship, or legal responsibility. The current IPR systems are mainly designed to safeguard works of human creativity, but with the introduction of AI-generated works, significant legal, ethical, and governance issues have arisen. This study explores the changing landscape of the relationship between Generative Artificial Intelligence and Intellectual Property Rights, with a special focus on ownership, creativity, and patentability, as well as patent governance in the digital innovation economy. The paper uses a conceptual research approach and an analytical approach based on doctrinal legal analysis, comparative policy evaluation, and literature review of current literature, international legal cases, and new AI governance frameworks. It examines issues such as copyright conflicts with AI-generated works, claims to inventorship, ethical issues with data training, limits on current patent laws, and algorithmic accountability. The study also makes a valuable contribution to interdisciplinary discussion by incorporating law and management, innovation studies, and technology governance perspectives, and by offering conceptual suggestions for developing sustainable and responsible innovation ecosystems that leverage AI technologies.
Vikas Rana1; Prakash Meena2; Ravneet Singh2
The Dharamshala region of Himachal Pradesh has become an important cultural tourism destination because of its rich Himachali traditions, Tibetan influence, heritage temples, local cuisine, handicrafts, folk music, spiritual environment, and natural landscape. Cultural experiences significantly shape how tourists perceive a destination, their emotional connection, and their overall satisfaction during their visit. At the same time, the rapid rise of Artificial Intelligence (AI) in tourism services has changed how tourists get information, plan trips, enjoy experiences, and give feedback. AI technologies, including personalized recommendation systems, chatbots, virtual guides, sentiment analysis, and smart tourism platforms, increasingly affect tourist expectations and satisfaction. This study aims to explore how Himachali cultural experiences impact tourist satisfaction in the Dharamshala region and to look into the role of AI-enabled tourism services in improving cultural engagement and visitor experiences. The research will focus on cultural aspects like local festivals, traditional food, heritage attractions, folk art, spiritual practices, local hospitality, and the authenticity of the destination. It will also examine the factors that affect tourist satisfaction, such as service quality, cultural interaction, accessibility, digital tourism support, safety, destination image, and AI-based travel assistance. The study will use a quantitative research design based on primary data collected from 220 domestic and international tourists visiting Dharamshala, McLeod Ganj, and nearby tourism spots. A structured questionnaire using a five-point Likert scale will gather data through convenience and purposive sampling techniques. Statistical tools, including Descriptive Analysis, Reliability Test (Cronbach's Alpha), Exploratory Factor Analysis (EFA), Correlation Analysis, and Multiple Regression Analysis, will analyze the relationship between cultural experiences, AI-based tourism services, and tourist satisfaction. The study's findings are expected to emphasize the growing importance of combining AI technologies with regional cultural tourism to improve tourist satisfaction and promote sustainable destination development. This research will contribute to smart tourism, cultural heritage management, and AI-driven hospitality practices while offering practical insights for tourism stakeholders and policymakers in Himachal Pradesh.
Dana-Marie Ramjit1
The governance of artificial intelligence presents one of the most consequential institutional challenges of the contemporary era, yet prevailing policy responses remain anchored in hierarchical, state-centric models of that are structurally misaligned with the distributed, non-linear, and polycentric character of AI systems themselves. This paper argues that heterarchical governance offers more analytically precise and practically relevant tool for understanding this misalignment that currently dominates AI governance discussions. Drawing on the author's prior work theorizing heterarchical models of digital governance and administrative legitimacy (Ramjit, 2025), the paper develops the concept of digital heterarchy as an original analytical framework for understanding how algorithmic authority operates across and between formal institutional hierarchies, generating accountability discrepancies and legitimacy crises that monocentric models are structurally ill-equipped to resolve. The paper advances three interconnected arguments. First, heterarchy reveals why AI systems resist hierarchical governance, their authority is distributed, contested, and operates simultaneously across multiple scales, rendering centralized oversight persistently reactive and institutionally inadequate. Second, existing AI governance arrangements, including the EU AI Act and emergent national regulatory regimes, reproduce the very hierarchical logic that digital heterarchy exposes as insufficient, creating governance gaps with significant consequences for democratic accountability and public trust. Third, the paper proposes that legitimate and socially responsible AI governance requires a relational, multi-scalar, and citizen-centered institutional design, rooted in heterarchical principles rather than hierarchical command. The paper concludes with comparative implications for institutional design across the EU, Canada, the Balkans, and the MENA region, with particular attention to the practical relevance of heterarchy theory for policymakers, regulators, and scholars navigating an era of contested algorithmic authority.
Pedro Pinheiro1; José Luis Silva2
Artificial intelligence has become a strategic priority for governments, universities and firms, but its responsible adoption depends not only on technological capacity but also on institutional governance. This paper examines national readiness for AI governance using open international data from the International Monetary Fund's AI Preparedness Index, complemented, where relevant, by policy information from the OECD.AI Policy Observatory and the Oxford Insights Government AI Readiness Index. Focusing on the relationship between digital infrastructure, human capital, innovation capacity and regulation and ethics, the study analyses how countries differ in their ability to support responsible and trustworthy AI. The empirical analysis compares selected European countries with different levels of economic development, digital maturity and regulatory capacity. Particular attention is given to the governance dimension, including ethical regulation, institutional coordination, accountability and the capacity to manage risks related to privacy, bias, transparency and labour market disruption. The analysis shows that AI readiness is uneven and multidimensional. Countries with stronger innovation systems and digital infrastructure do not automatically present equivalent strengths in ethical governance, while countries with emerging AI ecosystems may face particular challenges in translating policy ambition into institutional capacity. The paper contributes to interdisciplinary debates on AI in society by shifting attention from adoption to governance. It argues that responsible AI requires more than technical investment: it depends on regulatory preparedness, public sector capacity, skills development and mechanisms for social accountability. The findings are relevant for policymakers, educators and institutional leaders seeking to align AI development with ethical principles, social responsibility and inclusive digital transformation.
Perspectives for artificial intelligence-based financial analysis
Asma Hajji1
This study examines the joint influence of environmental factors and U.S. financial markets on the returns of Bitcoin (BTC) and Ethereum (ETH). The research investigates how environmental indicators, including CO₂ emissions, environmental innovation, and ESG performance, interact with major U.S. financial market indices to affect cryptocurrency returns. The analysis incorporates key financial indicators such as the S&P 500, NASDAQ, Dow Jones Industrial Average, gold prices, and oil prices, together with environmental variables, in order to provide a comprehensive understanding of the determinants of cryptocurrency performance. The study is situated within the broader context of technological developments in financial analysis, particularly the growing use of artificial intelligence in processing large datasets and supporting financial forecasting. While the research relies on conventional quantitative and econometric methods, its findings provide useful insights that may contribute to future AI-based financial analysis and prediction models in cryptocurrency markets. By combining environmental and financial perspectives, this research contributes to the growing literature on sustainable finance and digital assets. The study seeks to provide a deeper understanding of the factors shaping cryptocurrency returns and to highlight the potential value of advanced analytical technologies in studying the evolving dynamics of financial markets.
Mohamad Ali AlHussam1
This paper challenges the conventional paradigm of crisis management by demonstrating that global economic and political turbulence should not be treated as an exogenous shock for managers to navigate, but rather as a systemic condition influenced by corporate decisions. Investigating how sustainability education prepares current (executive MBA) and future (undergraduate) business leaders for global volatility, the study presents four key insights. First, both cohorts recognize that traditional global supply chain practices accelerate global turbulence, whereas sustainable practices serve a mitigative function. Second, exposing students to supply chain sustainability benchmarks actively develops global awareness and managerial empathy. Third, empirical findings indicate that gender-balanced environments optimize sustainability-focused strategic decision-making. Finally, the research reveals that threshold learning can occur independently through structured self-reflection, bypassing the strict requirement for direct instructor facilitation. Ultimately, this study establishes that integrating sustainability into management curricula empowers leaders to understand how their supply chain choices directly amplify or alleviate macroeconomic instability.
Svetlana Veljanovska1
The rapid advancement of generative artificial intelligence (AI) is transforming higher education by offering new opportunities for teaching, learning, assessment, and research. AI-powered tools are increasingly used by students and academic staff to support content creation, information retrieval, data analysis, and academic writing. While these technologies can enhance efficiency and accessibility, their growing adoption raises important legal and ethical concerns. This paper examines the key legal and ethical challenges associated with the use of generative AI in higher education. Particular attention is given to issues of academic integrity, authorship, intellectual property rights, data protection, transparency, and accountability. The study further explores the emerging European regulatory framework governing artificial intelligence and its implications for higher education institutions. In this context, the paper analyzes the need for institutional policies and ethical guidelines that can ensure the responsible use of AI while preserving fundamental academic values. Special consideration is given to the opportunities and challenges faced by higher education institutions in North Macedonia in aligning their practices with European standards and regulatory developments. The paper argues that the effective integration of generative AI requires a balanced approach that combines technological innovation with legal compliance, ethical responsibility, and academic integrity. Such an approach is essential for fostering trust, promoting quality education, and ensuring the sustainable use of AI technologies in higher education.
J V P Udaya Deepika1
The integration of the Internet of Things (IoT) presents valuable opportunities for small businesses to strengthen their brand identity through data-driven insights. This chapter mainly focuses on the application of Time Series Forecasting techniques to analyse the temporal patterns within data generated by IoT devices deployed in small business environments. By examining trends in customer interactions, operational metrics, and product usage captured through IoT sensors, time series forecasting can reveal crucial insights into customer behaviour, predict future demand, and optimise service delivery. This research investigates the potential of algorithms such as ARIMA, Exponential Smoothing, and Prophet to extract meaningful patterns from IoT data streams, enabling small businesses to personalise their branding efforts, anticipate customer needs, and ultimately foster stronger brand loyalty. The findings aim to provide a practical understanding of how small businesses can effectively leverage time series forecasting on IoT data for impactful brand development.
A management perspective on ethics and social responsibility
Ridhima Gupta1
The rapid integration of Artificial Intelligence (AI) into organizational operations has created significant opportunities for innovation while raising important ethical and social concerns. Responsible AI adoption has therefore emerged as a critical management issue, requiring organizations to ensure that AI systems are developed and used in ways that are fair, transparent, accountable, and aligned with societal values. This paper explores Responsible AI from a managerial perspective, focusing on the strategies and practices that support ethical decision-making and corporate social responsibility in AI-driven environments. It discusses the role of leadership in establishing governance structures, promoting ethical awareness, managing potential risks, and fostering stakeholder trust. The study also examines challenges associated with AI implementation, including bias, privacy protection, explainability, and regulatory compliance. By synthesizing contemporary research and management insights, the paper highlights the need for a holistic approach that combines technological capabilities with ethical oversight and organizational responsibility. The analysis suggests that organizations that proactively embed responsible AI principles into their strategies are better positioned to achieve sustainable growth, strengthen stakeholder confidence, and contribute positively to society. The study concludes that responsible AI adoption is not solely a technical requirement but a strategic management responsibility essential for long-term organizational success.
Undergraduate students' encounters with dark patterns
Bodhisattwa Bardhan Choudhury1
Dark patterns, or misleading user interface designs that discreetly control consumer behaviour against their own interests, are becoming more common in India as digital commerce grows. There is an urgent need to evaluate how common consumers identify, encounter, and react to such activities in light of the Central Consumer Protection Authority's (CCPA) November 2023 Guidelines, which listed thirteen categories of dark patterns under the Consumer Protection Act, 2019. This study looks on undergraduate students' experiences with dark patterns in Indian e-commerce and digital platforms. Undergraduate students are a digitally active but potentially vulnerable consumer demographic. A structured online questionnaire with three sections was used to gather data from 184 undergraduate respondents using a cross-sectional survey design. These sections included demographic profiling, an experiential rating of specific dark pattern encounters (such as false urgency, basket sneaking, drip pricing, subscription traps, and disguised advertisements), and an assessment of attitudes regarding ethics, trust, and regulatory necessity. A five-point Likert scale was used to evaluate responses, and descriptive statistics and inferential tests including chi-square, t-tests, and ANOVA were used for analysis. Findings reveal the frequency and impact of dark patterns on user trust and decision-making, and highlight a significant gap between digital confidence and actual awareness of manipulative design. The study underscores the need for consumer education and stronger regulatory enforcement to safeguard digital consumer rights among India's youth.
A comparative evaluation of SARIMA, LSTM, XGBoost and MLP models
Babafemi Daniel Ogunbona1; Anas Ibrahim1
Reliable inflation forecasting is central to monetary policy formulation and macroeconomic management, particularly in structurally volatile economies. This study comparatively evaluates four forecasting models - Seasonal Autoregressive Integrated Moving Average (SARIMA), Long Short-Term Memory (LSTM) networks, Extreme Gradient Boosting (XGBoost) and Multilayer Perceptron (MLP) - applied to monthly headline inflation rate of Nigeria from January 2009 to November 2024 (191 observations). An 80:20 temporal train-test split was applied uniformly across all models. Stationarity was established after second-order differencing and seasonal analysis confirmed an annual cycle at lag 12. MLP achieved the best forecasting performance across all evaluation metrics (RMSE = 1.02, MAE = 0.86, MAPE = 3.62%, R² = 0.975), outperforming other models. XGBoost performed poorly on the test set (RMSE = 8.94, R² = -0.892) despite strong cross-validation performance (CV RMSE = 1.20), a divergence attributed to the structural inflation break of 2022–2025 that exceeded the model's training distribution range. These findings establish MLP as the most accurate standalone model for Nigeria's inflation series and demonstrate that architectural simplicity, when appropriately calibrated, can outperform more complex deep learning architectures on small macroeconomic datasets.
Amal Kouri1; Ghofrane Barhoumi1; Jamel-Eddine Gharbi1
This research examines the factors underlying the low level of economic complexity in Tunisia and, as a result, the decline in the diversification of its exports. Based on complexity theory, we studied the influence of business sophistication, market efficiency, and productive human capabilities. The data used come from the global competitiveness, global innovation and global prosperity reports. The results show that export diversification can only be reversed through a profound transformation of capabilities, business sophistication and market efficiency.
Chayma Mehouachi1; Jamel-Eddine Gharbi2
The objective of our study is to verify the extent to which the adoption of Artificial Intelligence by companies influences their marketing capabilities and performance. Data collection was carried out by sampling. Following the verification of the dimensionality, reliability and validity of the measurement scales, we checked our structure model. The results show a significant positive impact of the adoption of Artificial Intelligence on performance and a significant impact mediated by marketing capabilities.
Sibangiso Ngwenya1; Thembekile Blessmore Ndebele2; Samkeliso Suku Dube2; Caroline Mhlanga2; Admire Bhuru2; Sinqobile Caren Dube2
The rapid growth of investment in artificial intelligence (AI) has created a highly uneven global funding landscape, with capital concentrated largely in a small number of high-income economies. Despite increasing interest in AI's potential contribution to development, comparatively little is known about how AI investment is distributed across the Global South. This paper presents a secondary data analysis of funding disparities within AI startup ecosystems in Sub-Saharan Africa, Latin America and Southeast Asia, benchmarked against investment patterns in OECD member states, which are used here as a proxy for high-income innovation economies. Data were drawn from publicly available venture capital datasets, harmonised to United States dollars and deduplicated across sources. The findings reveal significant inequalities in AI investment across regions. As of mid-2025, 159 AI startups in Sub-Saharan Africa had collectively raised US$803.2 million in external funding between 2020 and mid-2025. The analysis also highlights disparities in funding concentration, sectoral focus and investment stage progression. In Sub-Saharan Africa, more than 55% of AI startup activity is concentrated in fintech, talent technology and education, while investment in firms developing foundational AI technologies remains limited. Similar patterns of application-layer concentration and pre-seed stage dominance are evident in Latin America and Southeast Asia. Drawing on decolonial AI theory, this paper argues that these disparities reflect broader patterns of structural exclusion within the global innovation economy. The study concludes by outlining policy recommendations to strengthen Global South participation in AI development through targeted investment and improved access to AI infrastructure.
Emerging skills and employability challenges for future graduates
Esra Tracey1
This study examines the growing influence of artificial intelligence on employment patterns, professional roles, and graduate readiness. It explores how AI-driven automation is reshaping the nature of work by transforming routine tasks, redefining job expectations, and creating demand for new forms of human expertise. The study identifies occupations most exposed to technological disruption while also highlighting emerging career opportunities linked to data analysis, AI literacy, digital ethics, human-machine collaboration, creativity, and complex problem-solving. It also discusses the widening gap between traditional university preparation and the evolving skills required in an AI-driven economy. Particular attention is given to the role of higher education institutions in redesigning curricula, strengthening practical learning, and helping students develop adaptable, future-oriented competencies. Ultimately, the study argues that graduate employability in the age of AI depends not only on technical knowledge, but also on lifelong learning, ethical awareness, and the ability to work intelligently alongside emerging technologies.
Opportunities, risks and governance challenges
Victor Olusoji1; Nkemdilim Iheanachor2
The possibilities of using AI to change the way the public sector works and improve the services it provides are a phenomenon that continues to be investigated by governments globally (Wirtz et al., 2019). AI offers transformative opportunities in Nigeria, a country with long-standing governance issues of inefficiency and bureaucracy that are stifling development. This paper presents an extensive analysis, which will include policy document analysis, as well as a case study analysis of pilot projects for AI in agencies in Nigeria. The findings shall shed more light on how AI can deliver measurable gains, from automatically processing citizen applications to predicting resource allocation and fraud in public expenditure. But we project that critical risks may arise: algorithmic bias can perpetuate discrimination norms; opaque AI decision-making can diminish accountability and citizen trust; public AI systems are potentially vulnerable to data breaches and cyberattacks; and AI usage can create a greater digital divide if it is not accompanied by an investment in digital literacy (Sun & Medaglia, 2019). The outcome of this paper calls for the establishment of a complete governance system, including ethical guidelines, algorithmic impact assessments, public procurement standards for AI and institutional capacity development in the field of regulation and oversight (Floridi et al., 2018). The study aims to add to the comparative AI governance literature by analysing the Nigerian context and how it differs from emerging international governance frameworks, thereby shedding light on the unique challenges of developing democratic AI governance systems.
Legal and ethical challenges of AI governance
Racha Zeghmar1
Artificial intelligence (AI) is rapidly transforming societies, workplaces, and decision making processes, creating new opportunities while also raising complex questions about governance, regulation, rights, and accountability. This paper explores the challenges of legal responsibility and liability in the age of AI, focusing on the difficulty of determining who should be held accountable when AI systems cause harm, produce biased outcomes, or operate in unpredictable ways. It examines how traditional notions of responsibility are being challenged by increasingly autonomous technologies and highlights the need for effective regulatory frameworks that balance innovation with the protection of human rights. The paper argues that responsible AI development requires transparent, ethical, and human centered governance involving policymakers, legal institutions, technology developers, and society. Addressing AI liability is not only a legal issue but also a broader challenge that will influence the future of work and human responsibility in an increasingly automated world.
India and the architecture of a Global South AI model
Mitrajit Biswas1
As artificial intelligence (AI) rapidly develops, standard global frameworks remain expensive and concentrated within a few Western tech monopolies, often leaving developing nations behind. This paper examines India's vital role in architecting a distinct "Global South Model of AI" that prioritizes affordability, inclusivity, and public welfare. Rather than acting as a passive consumer, India leverages its proven Digital Public Infrastructure (DPI) philosophy to build an accessible, public-good technology ecosystem. Central to this vision is the state-backed IndiaAI Mission, which expands sovereign computing capacity, alongside the "Trusted AI Commons"—an open-source repository of datasets and language models designed specifically for resource-constrained countries. This structural shift was highlighted at the India–AI Impact Summit in New Delhi, positioning India as a strategic bridge for technical cooperation across Africa and Southeast Asia. Built on the principles of equitable progress, India's model challenges commercial monopolies by championing open science, algorithmic transparency, and energy-efficient AI systems tailored for low-resource environments. Furthermore, India's regulatory frameworks, including the Digital Personal Data Protection Act, offer a unique mechanism for ethical data governance outside traditional US-China rivalries. Ultimately, by shifting the global narrative from corporate profits to scalable public utility, India provides the Global South with a reproducible blueprint for digital sovereignty. This approach ensures that emerging economies can leverage AI for sustainable socio-economic development without sacrificing their technological independence.
Joudy Zayat1
Artificial intelligence is becoming a powerful tool in pharmaceutical supply planning by helping factories predict raw material demand more accurately. Instead of relying only on manual estimates, AI can analyze past production data, seasonal trends, supplier performance, and market changes to forecast what materials will be needed and when . This helps reduce shortages that can stop production and overstocking that can waste money and storage space .In a medicine factory, better supply planning is important because delays or missing ingredients can affect the whole production line. AI can support planners by detecting risks early, adjusting inventory levels, and improving order timing for raw materials . As a result, the factory can work more efficiently, lower costs, and maintain a more stable supply of medicines for patients.
The place of critical thinking, judgement and imagination in the age of artificial intelligence(AI)
Rodgers Otieno Odhiambo1
The emergence of Artificial Intelligence (AI) in the global stage, has dramatically and significantly transformed not only how knowledge is created, but also accessed, and assessed. This change is especially noticeable in higher education, where research, instruction and learning are increasingly incorporating AI-powered tools like ChatGPT and large language models (LLMs). This paper examines the impact of Artificial Intelligence (AI) on legal education and practice in Kenya, highlighting its influence on critical thinking and human judgment. The paper discusses the transformative effects of Artificial Intelligence, such as increased access to information and the automation of tasks, while raising concerns of about its limitations in replicating essential human abilities like critical and evaluative judgement. The paper calls for a re-evaluation of traditional pedagogical frameworks in this new technological landscape. While AI tools like ChatGPT offer significant benefits such as automating research and improving efficiency, they also pose serious risks to traditional legal practice and education. The legal field ultimately depends on ethical judgment, nuanced reasoning, and interpretive analysis. AI cannot replicate these distinctively human faculties. As rapid AI integration continues, it is critical that academics and legal practitioners preserve and foster fundamental human skills, particularly evaluative judgment and critical thinking, within traditional pedagogical frameworks. This is because critical thinking in legal education is often associated with skill-based activities, including analyzing, synthesizing, judging critically, and evaluating problems.
Aditya Singh1; Cristina Raluca Gh. Popescu2
The development of economies, public services, in addition to governance is being transformed by AI in the recent years, which allows the immediate requirement for ethical frameworks along with regulatory policies which are effective ones. The AI Act helps European counties to have founded a thorough regulatory method which is on the basis of risk, whereas the Asian countries have implemented diverse strategies of governance which indicates distinct technological ecosystems, economic priorities, as well as legal systems. This study will explore the existing gaps in the literature, and it will evaluate principles which are ethical ones, regulation of AI, as well as governance methods in a comparative manner across both above mentioned continents via present evidence. The paper will examine the main dimensions which will comprise of human oversight, transparency, regulatory enforcement, accountability, privacy, in addition to fairness. This study will detect the continuing governance hurdles, regional differences and similarities which affects the execution of responsible AI in such countries. Then, the paper will talk about for large scale cooperation at the international level in the governance of human centered AI which is still advancing & adaptive in nature, when considering the potential opportunities, in order to balance the concerned ethical responsibility along with novelty.
Cristina Raluca Gh. Popescu1; Aditya Singh2
The digital transformation, intelligent decision making, in addition to automation, are all leading towards the reshaping of workplaces by AI in the recent years, which not only generates novel opportunities but also raises worries about the fairness, rights of the workers, governance in regard to employment, as well as algorithmic accountability. The past few years showed regulatory progress especially in European countries, which presents increasing awareness to guarding the workers employed in places where the management of the workplace is assisted by AI and its linked risks, while the hurdles on the path of its incorporation still exist. This study will perform an analysis especially a comparative one in the context of workplace modernization led by AI in not only developing but also developed countries via several available evidence, after exploring the existing gaps in the literature. The paper will explore the ways in which distinct regulatory as well as economic contexts affects the implementation of Artificial Intelligence, ethical governance, protection of employees, adaptation of organizations, in addition to reskilling of workforce. The paper will detect various hurdles associated with human oversight, bias, privacy, as well as transparency, when discussing policy methods to encourage comprehensive in addition to responsible transformation of workplaces. The paper will aid in setting up recommendations which are of practical nature to balance social responsibility when proceeding with technological novelty along with rights of workers, to assist sustainable implementation of AI on a large scale comprising of different countries.