Governments Likely Won't be Very Good at AI Regulation

Artificial intelligence regulations are at an early stage, and some typical areas of enforcement, such as copyright or antitrust, will take some time to develop, in the former case because a sufficient body of precedent from prior cases must develop; in the latter case because markets will not be developed enough to make commercial power determinations. 

In the meantime, regulators seem to be focusing on the procedural areas: consumer safety (AI use for autonomous vehicles); privacy (when facial recognition can be used) ; security (use of personal data); transparency (how models have used training data); algorithmic bias; or liability (who is responsible if harm occurs?). But we remain early in those processes as well. 

Area of Interest	Description	Example Regulations
Safety and Security	Focuses on mitigating risks associated with AI systems, such as malfunctions, biases, and security vulnerabilities.	Mandates for robust testing and safety assessments of high-risk AI systems. - Regulations on the use of AI in critical infrastructure or autonomous vehicles.
Privacy	Protects individual privacy rights in the context of AI data collection, use, and decision-making.	Alignment with existing data privacy laws (e.g., GDPR, CCPA). - Restrictions on AI systems that use personal data for profiling or decision-making.
Transparency and Explainability	Aims to make AI systems more transparent and understandable, allowing for human oversight and accountability.	Requirements for developers to disclose how AI systems function and the data they use. - Right to explanation for individuals impacted by AI-driven decisions.
Algorithmic Bias	Addresses the potential for bias in AI algorithms due to training data or design choices.	Regulations on fairness and non-discrimination in AI algorithms used for hiring, loan approvals, etc. - Auditing of AI systems to identify and mitigate bias.
Liability and Accountability	Defines who is responsible for the actions and decisions of AI systems, particularly in cases of harm.	Clarification on liability for accidents or errors caused by autonomous AI systems. - Establishment of responsible actors in the AI development and deployment process.
Intellectual Property	Addresses ownership and rights related to AI creations (e.g., inventions, copyright in creative outputs).	Clarification on patentability of AI-generated inventions. - Determination of authorship rights for creative content produced by AI.

Some of us remain skeptical about government ability to usefully regulate important and profound new technologies, so hopefully overreach will not happen. 

Title	Authors	Publication	Key Takeaways
The Myth of the Digital Regulatory State	Sheila A. Brennan & Michael J. Meurer	Virginia Journal of Law & Technology	Argues that rapid technological change outpaces traditional regulatory frameworks. - Regulations often struggle to keep up with the evolving nature of new technologies.
Regulating Artificial Intelligence: A Multi-Stakeholder Approach	Daniel W. Drezner	Global Policy	Highlights the complexity of AI and the difficulty in defining and addressing potential risks. - Suggests a multi-stakeholder approach involving governments, industry, and civil society.
Can Technology Be Governed?	David Kaye & Debbie Sykes	Daedalus	Discusses the challenges of applying traditional governance structures to complex, globalized technologies. - Raises concerns about democratic control and potential for censorship.
The Innovation Paradox: How Global Governance Fails to Catch Up with Technological Change	Robert Falkner	Brookings Institution	Examines how rapid innovation creates challenges for international cooperation on regulation. - Suggests the need for more flexible and adaptable regulatory approaches.
The Case for Algorithmic Regulation	Daniel Kreutzfeldt	Oxford Internet Institute	Proposes a focus on algorithmic decision-making processes rather than specific technologies. - Argues for transparency and accountability in algorithms used by companies and governments.