Chapter 21: Further Reading — Corporate Governance of AI

Organized by topic. Annotations describe each source's key contribution and appropriate audience.

Foundational Frameworks and Principles

1. National Institute of Standards and Technology (NIST). AI Risk Management Framework (AI RMF 1.0). NIST, 2023. The US government's comprehensive framework for managing AI risk across the AI lifecycle. Provides a structured vocabulary (Govern, Map, Measure, Manage) that has become a de facto standard for AI governance programs. Essential reading for compliance and risk management professionals building AI governance programs.

2. OECD. OECD Principles on Artificial Intelligence. OECD, 2019 (updated 2023). The foundational international AI principles framework, endorsed by 42 countries. Provides the conceptual basis for much AI governance regulation. Important for understanding the international convergence — and divergence — in AI governance frameworks. Useful for global organizations navigating multi-jurisdictional AI governance.

3. Microsoft. Microsoft Responsible AI Standard, v2. Microsoft Corporation, 2022. One of the most detailed and operationally specific corporate AI ethics frameworks publicly available. Translates abstract principles into engineering requirements and documentation standards. Essential reading for practitioners designing internal AI governance programs — compare with your organization's own principles to assess the specificity gap.

4. Raji, I. D., and Buolamwini, J. "Actionable Auditing: Investigating the Impact of Publicly Naming and Shaming in Algorithmic Audit." Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, 2019. Empirical study of how public disclosure of AI bias audit findings changes corporate behavior. Foundational evidence for the importance of external accountability in AI governance. Essential for understanding the governance role of external auditing.

Ethics Boards and Governance Bodies

5. Canca, C. "Why AI Ethics Principles Fail and What to Do About It." AI & Society, 2022. Systematic analysis of why AI ethics principles documents fail to produce ethical AI practice, focusing on implementation gaps and enforcement deficits. Provides a rigorous analytical framework for evaluating the substantiveness of AI governance structures.

6. Metcalf, J., Moss, E., Watkins, E. A., Singh, R., and Elish, M. C. "Algorithmic Impact Assessments and Accountability: The Co-construction of Impacts." Proceedings of the ACM Conference on Fairness, Accountability, and Transparency (FAccT), 2021. Examines algorithmic impact assessments as governance tools: their design, limitations, and the conditions under which they produce accountability. Essential reading for anyone designing or evaluating pre-deployment review processes.

7. Jobin, A., Ienca, M., and Vayena, E. "The Global Landscape of AI Ethics Guidelines." Nature Machine Intelligence, vol. 1, 2019, pp. 389–399. Systematic analysis of 84 AI ethics guidelines from organizations around the world, identifying common themes and significant gaps. Important for understanding the scope and limitations of the global AI principles landscape. Valuable context for executives comparing their organization's principles to industry norms.

Responsible AI Functions and Practices

8. Rakova, B., Yang, J., Cramer, H., and Chowdhury, R. "Where Responsible AI Meets Reality: Practitioner Perspectives on Enablers for Shifting Organizational Practices." Proceedings of the ACM on Human-Computer Interaction, 2021. Qualitative research on what responsible AI practitioners actually do in organizations, the organizational enablers and barriers they face, and how organizational culture shapes the practice of AI ethics. One of the most useful empirical studies of how AI governance works (and doesn't) in practice.

9. Morley, J., Cowls, J., Taddeo, M., and Floridi, L. "Governing the Transition to Responsible AI." Science and Engineering Ethics, 2021. Analysis of the organizational and institutional changes required to make responsible AI practice a norm rather than an exception. Provides practical guidance for organizations seeking to advance their governance maturity.

10. Amershi, S., et al. "Software Engineering for Machine Learning: A Case Study." Proceedings of the International Conference on Software Engineering (ICSE), 2019. Microsoft Research's analysis of how machine learning development differs from traditional software engineering, with significant implications for governance process design. Technical but accessible; essential for understanding why AI governance processes must be designed differently from software quality assurance.

Board Governance and Executive Oversight

11. Tricker, R. I. Corporate Governance: Principles, Policies, and Practices. 4th ed. Oxford University Press, 2019. Comprehensive reference on corporate governance principles and practice. While not AI-specific, provides the governance theory framework necessary to understand why and how AI governance fits into the broader corporate governance system. Recommended background reading for board members and governance professionals new to the field.

12. World Economic Forum. AI Governance Alliance: Briefing Papers. WEF, 2023. Series of briefing papers from WEF's multi-stakeholder AI governance initiative, covering board oversight, responsible AI standards, and governance frameworks for specific sectors. Accessible and practically oriented — appropriate for executives and board members seeking an introduction to AI governance concepts without deep technical content.

Data Governance

13. Gebru, T., Morgenstern, J., Vecchione, B., Vaughan, J. W., Wallach, H., Daumé, H., and Crawford, K. "Datasheets for Datasets." Communications of the ACM, vol. 64, no. 12, 2021, pp. 86–92. The seminal proposal for standardized documentation of AI training datasets. Essential reading for anyone designing data governance programs for AI. The dataset documentation standard this paper proposes has been widely adopted and is foundational to responsible AI data practice.

14. European Data Protection Board. Guidelines on Automated Decision-Making and Profiling. EDPB, 2018 (updated). Authoritative guidance on GDPR requirements for AI-based decision-making, including the right to explanation and human review requirements. Essential for legal and compliance professionals operating in or with the EU market.

Incentives, Culture, and Organizational Behavior

15. Edelman, B., and Geradin, D. "Efficiencies and Regulatory Shortcuts: How Should We Regulate Companies Like Airbnb and Uber?" European Competition Journal, 2016. While focused on platform companies rather than AI specifically, this analysis of how fast-moving technology companies manage regulatory pressure provides important context for understanding the organizational incentive dynamics that AI governance must navigate.

16. Edmondson, A. C. The Fearless Organization: Creating Psychological Safety in the Workplace for Learning, Innovation, and Growth. Wiley, 2018. Foundational work on psychological safety — the organizational condition that makes it possible for employees to raise concerns without fear of punishment. Essential reading for anyone seeking to understand why ethics concerns go unraised and what organizational interventions can change that dynamic.

Vendor Governance and Procurement

17. Reisman, D., Schultz, J., Crawford, K., and Whittaker, M. Algorithmic Impact Assessments: A Practical Framework for Public Agency Accountability. AI Now Institute, 2018. Focuses on public sector AI procurement but contains analysis of algorithmic impact assessment design applicable to private sector procurement governance. Important for understanding what "adequate due diligence" looks like in AI procurement.

18. General Services Administration (US). AI Acquisition Guidance: A Primer for Federal Acquisition Professionals. GSA, 2021. Federal guidance on AI procurement requirements and best practices. Directly applicable to government contractors; also useful as a benchmark for private sector procurement governance, as government requirements often foreshadow broader industry standards.

Case Studies and Empirical Research

19. Whittaker, M., Crawford, K., Dobbe, R., Fried, G., Kaziunas, E., Mathur, V., Myers West, S., Richardson, R., Schultz, J., and Schwartz, O. AI Now Report 2018. AI Now Institute, 2018. One of the first comprehensive empirical assessments of AI's social impacts and governance gaps. While now several years old, it remains important as a historical document establishing the empirical foundation for many subsequent AI governance debates.

20. Coeckelbergh, M. AI Ethics. MIT Press, 2020. Accessible philosophical treatment of AI ethics that provides the conceptual foundations underlying AI governance practice. Appropriate for practitioners seeking deeper grounding in the ethical theory behind governance frameworks. Not a governance manual, but essential context for understanding why governance matters and what it should accomplish.