Handbook editors
Payal Kumar, ISH, India
Pawan Budhwar, Aston University, UK
Elham Malik, NIT Andhra Pradesh, India
Publisher: World Scientific Publishers
Executive Editor: Gideon Markman, USA
Introduction: Rethinking Ethics in the Age of Artificial Intelligence
Artificial intelligence (AI) is transforming organizations, governance, and the lives of people worldwide. The handbook grounds itself in digital colonialism as an analytical paradigm for interpreting AI and global inequality. We conceptualize digital colonialism as a process that entails the concentration of power, value, and knowledge in the hands of a few actors, leading to technological dependency. In particular, we dwell on three mutually dependent mechanisms, namely, data extraction, infrastructure control, and value asymmetry, that determine the way AI systems can generate and allocate benefits and burdens. This lens allows us to move beyond generalized discussions of AI ethics and foreground the structural forces that reproduce global inequalities through AI.
AI is increasingly embedded in the systems that organize social, institutional, and economic life, including algorithmic decision-making in organizational systems and generative AI systems that facilitate knowledge building. Although AI systems are commonly portrayed as unbiased, objective, and efficiency-enhancing, they are inevitably linked to political economies, institutional logics, and historical injustices that shape the nature of data, knowledge, and value production and distribution. The rapid advancement of AI raises fundamental ethical concerns about power, responsibility, agency, equity and also ethical decision making (Hiebert & Kumar, in press, 2026). Some pertinent questions are: Who owns AI systems, what knowledge is coded into them, and who profits - or gets sidelined? These are not merely technical questions but are normative concerns that require a multi-disciplinary engagement with ethics, organizational theory, sociology and political economy.
AI Ethics, Digital Colonialism, and Global Inequality
Fairness, accountability, transparency, and privacy are among the principles highlighted in mainstream AI ethics literature (Jobin et al., 2019; Mittelstadt, 2019). These principles have been of immeasurable value in defining algorithmic harms. However, they tend to view ethics as a technical issue rather than as inherently connected to the power structures that inform AI systems. This handbook is divided into four themes:-
i. AI Ethics and Digital Colonialism
Digital colonialism offers an understanding of how AI systems replicate historical processes of extraction and dependence. The central part of this regime is digital extraction, a perpetual act of mining and refining information into economic and political wealth. Such extraction processes enhance economic dependency and inequality, promote cultural hegemony, and undermine political independence. The context of AI in this system clarifies the constraints of principle-based ethics: the asymmetry of information, infrastructure, and value is not a technical incident but an organizational consequence of world power relations (Khan, 2025).
ii. Data Extraction and Surveillance
In a digital colonial context, data mining is both a source of economic activity and a control instrument. This dynamic is best exemplified by the so-called surveillance capitalism, in which human experience is industrialized through big-data harvesting and behavioral prediction (Zuboff, 2019). Surveillance based on AI is spreading across security, healthcare, and city management, often without sufficient transparency or regulation (Blease, 2024; Saheb, 2023), posing challenges to privacy, autonomy, and democratic accountability. Algorithms are biased and discriminatory against marginalized groups, and AI systems reproduce inequalities among races, genders, and social classes (Buolamwini & Gebru, 2018; Benjamin, 2019; Noble, 2018). Recent literature emphasizes that fairness should account for intersecting identities and socio-technical systems (Gohar & Cheng, 2023). Generative AI presents novel epistemic dangers, including hallucinations, misinformation, and unreliable outputs, that may be harmful in high-stakes settings like healthcare, learning, and research (Ashwin et al., 2025). These advancements highlight the necessity of strong governance, human control, and participatory design to address data extraction and its social impacts.
iii. Infrastructure Control and Dependency
Infrastructure control describes the process of concentrating AI platforms, cloud services, and foundational models in a few corporations and states. Such a concentration supports reliance that undercuts data sovereignty and constrains the ability of other areas to innovate. The internet regime, with its tenacity of accumulation, surveillance, and expansion, is the centralization of power and the repression of alternatives. According to Khan (2025), the global South's reliance on proprietary infrastructure replicates colonial dependencies. Complementary analyses demonstrate that algorithmic tools are incorporated into geopolitical and economic relations; they are usually used to serve the interests of dominant actors rather than to support local communities (Saheb, 2023). Generative AI can address platform dependency by managing access to training data, computational resources, and deployment models (Ashwin et al., 2025). Such observations have elicited calls to establish AI sovereignty and to invest in government infrastructure and open-source models.
iv. Value Asymmetry and Labor in AI
Value asymmetry explains how the benefits of AI are not equally distributed: the people who control data and infrastructure capture innovation rents, while data creators get little. Research on the human resources field shows that the use of AI in recruitment, appraisal, and monitoring can increase workplace inequality and reduce employee agency (Budhwar et al., 2022, 2023; Chowdhury et al., 2022, 2024). To address these imbalances, organizational scholars promote human values and reflexivity in algorithmic management (Budhwar et al., 2022, 2023). Maheshwari et al. (2025) suggest applying non-Western ethical theories, such as the Dharma Matrix, to create context-specific decision-making. In the meantime, leadership reflexivity and pluralistic ethics focus on inclusivity and empowerment within organizations (Mascarenhas et al., 2023; Marques et al., 2024).
Positioning this Handbook: Ethics Beyond Technical Fixes
While the field of artificial intelligence (AI) ethics is expanding rapidly, much of the literature remains fragmented across analytical levels and disciplinary traditions. A substantial body of work continues to focus on principle-based frameworks such as fairness, accountability, transparency, and privacy, alongside technical remedies including bias mitigation, explainability, and compliance-oriented governance. Although these approaches have made important contributions, they often pay insufficient attention to the broader organizational, societal, and geopolitical contexts in which AI systems are designed, deployed, and governed. As a result, questions of power, inequality, labor, institutional accountability, and human agency remain underexplored in mainstream AI ethics debates.
More recent scholarship has begun to address these gaps by examining AI as a socio-technical phenomenon shaped by organizational practices, economic structures, and global asymmetries. However, these perspectives remain dispersed across disciplines. This handbook responds to that fragmentation by bringing together technical, organizational, and societal approaches to AI ethics within a single interdisciplinary volume. It treats ethics not as an external constraint but as an integral part of the design, development, deployment, and use of AI systems in context.
Although digital colonialism is a central theme, especially for understanding disparities in data, infrastructure, and technological power, the volume situates this concern within a broader ethical landscape that also includes privacy and surveillance, algorithmic bias and intersectional inequality, AI hallucinations and epistemic risk, and organizational transformation. By foregrounding organizational settings and engaging pluralistic, context-sensitive, and non-Western perspectives, the handbook aims to advance more responsible, equitable, and human-centered approaches to AI futures.
Like the other handbooks in the Set on Business Ethics and Values in a Globalized World (Editors Payal Kumar & Peter Bamberger), this handbook will consist of cutting-edge research that will serve as an indispensable guide to scholars. While the other handbooks center around the themes of i. human resource ethics ii. leadership and power dynamics and iii. value implementation and ethical strategies, this handset stands apart in that its focus is on rethinking ethics in the fast-changing world of artificial intelligence which gives rise to not only greater efficiencies and breathtaking innovations, but also to skewed global power dynamics. Following Baumeister's lead in encouraging scholars to break the hegemony of research consisting of good actions leading to good outcomes, and bad leading to bad, this handbook produces more nuanced research which reflects organizational complexities (Baumeister et al, 2001).
Indicative Themes and Topics
We invite theoretical papers, empirical studies (qualitative, quantitative, or mixed-methods), comparative analyses, and policy-oriented research on the following themes. Contributions should exhibit strong theoretical grounding, rigorous methodology, and practicality.
1. AI Ethics, Governance, and Human Agency
Examine the normative, institutional, and societal questions raised by AI, including governance, accountability, bias, epistemic risk, and human agency across organizational and public contexts.
Indicative chapters:
● Rethinking AI Ethics Beyond Technical Compliance
● Governance and Accountability in AI Systems
● Algorithmic Bias, Fairness, and Social Justice
● Intersectionality and Structural Discrimination in AI
● AI Hallucinations, Misinformation, and Epistemic Risk
● Privacy, Autonomy, and the Ethics of AI Surveillance
● Human Agency in AI-Augmented Decision-Making
● Ethics of AI in Healthcare, Education, and Public Services
● Leadership and Reflexivity in AI-Driven Organizations
● Pluralistic and Non-Western Frameworks for AI Ethics
● Responsible AI Regulation and Institutional Design
● Designing Human-Centered AI Systems
2. Data Extraction and the Unequal Geographies of AI
Analyze how AI depends on the large-scale extraction, aggregation, and commodification of data, producing uneven geographies of knowledge, consent, and control.
Indicative chapters
● Data Extraction and the Dynamics of Digital Colonialism
● The Political Economy of AI Training Data
● Data Supply Chains and Global Knowledge Appropriation
● Cross-Linguistic Inequality and the Politics of AI Datasets
● Consent, Ownership, and Collective Rights over Data
● Datafication of Everyday Life in the Global South
● Indigenous Knowledge, Data Governance, and AI
● Case Studies in Data Appropriation and Unequal Benefit
● Regulating Data Extraction and Building Equitable Data Regimes
● Knowledge Extraction and Uneven Data Infrastructures
3. Infrastructure Control and AI Dependency
Investigate how control over compute, cloud systems, platforms, models, and technical standards creates dependency and constrains autonomy in AI development and deployment.
Indicative chapters:
● Infrastructure Control and the Dynamics of Digital Colonialism
● Cloud Concentration and the Centralization of AI Capacity
● Compute Inequality and Barriers to AI Development
● The Politics of API Access and Platform Control
● Foundation Models and the Power to Set Technical Standards
● AI Dependency and the Limits of Technological Sovereignty
● The Geopolitics of Chips, Cloud, and AI Supply Chains
● Open-Source AI as a Pathway to Infrastructural Autonomy
● Public and Cooperative Alternatives to Proprietary AI Infrastructure
● Community-Led AI Infrastructures and Local Capacity Building
4. Value Asymmetry, Labor, and Unequal AI Futures
Explore how AI-generated value is distributed unevenly, with profits and strategic gains concentrated among a few actors while labor burdens, precarity, and social costs are displaced elsewhere.
Indicative chapters
● Value Extraction and Asymmetry in Digital Colonialism
● Who Captures Value in the AI Economy?
● Innovation Rents, Intellectual Property, and Global Inequality
● Human-in-the-Loop Work and AI Value Chains
● Content Moderation and the Hidden Workforce of AI
● Algorithmic Management and Worker Control
● AI, Employment Restructuring, and Regional Labor Divides
● AI in HRM and the Reproduction of Workplace Inequality
● Redistribution, Compensation, and Fairer AI Economies
● Cooperative, Commons-Based, and Worker-Centered AI Futures
Timeline
● 31st May, 2026: Abstract submission (800–1,000 words)
(Send to elhammalik77@gmail.com)
● 15th June, 2026: Decision notification
● 30 September 2026: Full chapter submission
● 15 December 2026: Review feedback
● 15 February 2027: Revised submission
● 15 April 2027: Final submission
● 1 May 2027: Final manuscript to publisher
Submission Guidelines
All manuscripts to follow APA 7th edition style (with no DOIs). Use British English with 'z' spellings. Each chapter to be between 8,000 to 8500 words, inclusive of references, tables and figures. If AI is employed in the paper, its purpose and application need to be clearly outlined in the document.
References
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