Reflective Activity 1: Ethics in Computing in the Age of Generative AI

Global AI Ethics Landscape: Challenges and Recommendations

The generative AI revolution has exposed critical gaps in global AI governance, revealing a fragmented landscape where consensus remains elusive despite widespread recognition of ethical imperatives. Correa et al.’s (2023) comprehensive analysis of 200 worldwide AI guidelines demonstrates that while 17 core principles have emerged—including transparency, justice, accountability, reliability, and privacy—their implementation remains predominantly voluntary and non-binding.

Current State and Divergences

The meta-analysis reveals significant geographical and institutional disparities in AI ethics approaches. While 98% of guidelines constitute “soft law” without legal obligations, only 4.5% propose binding regulations (Correa et al., 2023). This voluntary approach reflects what Deckard (2023) identifies as a fundamental challenge: the need for practical solutions that bridge abstract principles with real-world implementation.

This fragmentation echoes earlier ethical governance challenges identified in the Menlo Report, where Finn and Shilton (2023) demonstrated how establishing consensus on ethical principles requires sustained multi-stakeholder engagement. Similarly, Fjeld et al.’s (2020) mapping of AI principles revealed convergence on core values but divergence on implementation approaches—a pattern that persists in contemporary generative AI governance.

Particularly concerning is the underrepresentation of Global South perspectives, with Europe and North America dominating discourse (60.5% of documents), while Africa, South America, and parts of Asia remain marginalized (Correa et al., 2023). This imbalance perpetuates technological colonialism where AI ethics frameworks primarily reflect Western values and priorities, undermining the universal applicability of professional codes like the ACM Code of Ethics.

Recommended Course of Action

1. Mandatory Ethical Impact Assessments

Governments should implement legally binding requirements for AI ethical impact assessments, similar to environmental impact assessments. This addresses the current 96% reliance on voluntary normative guidelines without practical implementation mechanisms (Correa et al., 2023).

2. Multi-Stakeholder Global Governance Framework

Establish an international AI ethics body with proportional representation from all regions, particularly amplifying Global South voices. This framework should move beyond the current industry-dominated discourse where private corporations and governmental institutions equally contribute 48% of guidelines (Correa et al., 2023).

3. Interdisciplinary Professional Standards

Implement Deckard’s (2023) recommendation for interdisciplinary collaboration by requiring computing professionals to demonstrate competency in ethics, philosophy, and social sciences alongside technical skills. This aligns with established frameworks like the BCS Code of Conduct, which emphasizes professional competence and public interest, while addressing the current lack of practical implementation tools identified in the meta-analysis.

Legal Impact: Transitioning from voluntary guidelines to binding regulations would create enforceable accountability mechanisms, addressing the current regulatory vacuum where only 20% of documents propose government-administered regulation (Correa et al., 2023).

Social Impact: Inclusive governance frameworks would address algorithmic bias and discrimination affecting marginalized communities, moving beyond the current Western-centric approach that potentially reinforces existing inequalities.

Professional Impact: Mandatory ethical competency requirements would transform computing education and practice, aligning with Deckard’s (2023) emphasis on developing “practical solutions that can be implemented in real-world situations.” This approach reflects established research methodology principles (Dawson, 2015) while ensuring professionals can navigate competing ethical obligations as outlined in professional codes and industry frameworks like IBM’s Principles for Trust and Transparency.

Conclusion

The generative AI revolution demands urgent transformation from aspirational principles to actionable frameworks. The current reliance on voluntary guidelines has proven insufficient for addressing AI’s societal impact. Only through legally binding standards, inclusive global governance, and mandatory professional ethical competency can we ensure AI development serves humanity’s diverse needs rather than perpetuating existing power structures.

The time for “ethics-washing” through non-binding principles has passed. Computing professionals must embrace their responsibility to shape technologies that genuinely align with global human values, not merely Western corporate interests.

References

ACM (2018) ACM Code of Ethics and Professional Conduct. Association for Computing Machinery. Available at: https://www.acm.org/code-of-ethics (Accessed: 19 October 2025).

BCS (2021) The Chartered Institute for IT. British Computer Society. Available at: https://www.bcs.org/

Corrêa, N.K., Galvão, C., Santos, J.W., Del Pino, C., Pinto, E.P., Barbosa, C., Massmann, D., Mambrini, R., Galvão, L., Terem, E. and de Oliveira, N. (2023) ‘Worldwide AI ethics: A review of 200 guidelines and recommendations for AI governance’, Patterns, 4(10), 100857. Available at: https://arxiv.org/pdf/2206.11922 (Accessed: 19 October 2025).

Dawson, C. (2015) Projects in Computing and Information Systems: A Student’s Guide. Harlow: Pearson.

Deckard, R. (2023) ‘What are Ethics in AI?’, BCS, 3 April. Available at: https://www.bcs.org/articles-opinion-and-research/what-are-ethics-in-ai/ (Accessed: 18 October 2025).

Finn, M. and Shilton, K. (2023) ‘Ethics governance development: The case of the Menlo Report’, Social Studies of Science, 53(3), pp. 315-340. Available at: https://doi.org/10.1177/03063127231151708 (Accessed: 19 October 2025).

Fjeld, J., Achten, N., Hilligoss, H., Nagy, A. and Srikumar, M. (2020) Principled artificial intelligence: Mapping consensus in ethical and rights-based approaches to principles for AI. Berkman Klein Center Research Publication. Available at: https://cyber.harvard.edu/publication/2020/principled-ai (Accessed: 15 October 2024).

IBM IBM’s Principles for Trust and Transparency. Available at: https://www.ibm.com/policy/trust-transparency (Accessed: 19 October 2025).

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