What are the risks from AI? 

This week we spotlight the twentieth framework of risks from AI included in the AI Risk Repository: Wirtz, B. W., Weyerer, J. C., & Sturm, B. J. (2020). The Dark Sides of Artificial Intelligence: An Integrated AI Governance Framework for Public Administration. International Journal of Public Administration, 43(9), 818–829. https://doi.org/10.1080/01900692.2020.1749851

Paper Focus‍

This paper addresses the gap between rapid AI development and lagging governance capabilities. While AI advances quickly, public administration lacks adequate frameworks to respond to AI challenges and implement effective regulation to prevent harm. The authors develop an integrated governance framework to guide the regulatory process for AI applications in public administration.

Included Risk Categories

This paper presents an overview of AI challenges organized into 3 main categories: AI law and regulation, AI Society, and AI ethics. 

1. AI Law and Regulation Legal and regulatory challenges including governance of autonomous intelligence systems that operate as "black boxes," responsibility and accountability issues when AI systems make autonomous and unpredictable decisions, and privacy and data security concerns when AI systems collect data without proper consent or oversight.

2. AI Society Societal impacts including workforce transformation and substitution, social acceptance and trust issues that affect AI adoption, and human-machine interaction challenges that blur boundaries between humans and AI systems, potentially changing human behavior.

3. AI Ethics Ethical challenges including AI rulemaking for humans where rational AI decisions may have unintended consequences for affected individuals, AI discrimination through biased datasets that perpetuate prejudices, moral dilemmas where AI must choose between conflicting ethical values, and compatibility issues between machine and human value judgments.

Key features of the framework and associated paper:

• Regulation theory foundation: Unlike previous models, this framework is grounded in established regulation theory, treating AI challenges as market failures that require governmental intervention
• Five-layer governance structure: Provides a detailed five-layer framework including AI technology/services, AI challenges, AI regulation process, AI policy, and collaborative AI governance
• Detailed regulatory process: Offers a four-stage regulatory process (framing, risk and benefit assessment, risk evaluation, and risk management) with concrete implementation steps

⚠️Disclaimer‍

This summary highlights a paper included in the MIT AI Risk Repository. We did not author the paper and credit goes to Bernd W. Wirtz, Jan C. Weyerer, and Benjamin J. Sturm. For the full details, please refer to the original publication: https://doi.org/10.1080/01900692.2020.1749851.

Further engagement 

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