Beyond Benchmarks: On The False Promise of AI Regulation

Gabriel Stanovsky, Renana Keydar, Gadi Perl, Eliya Habba·January 26, 2025

Summary

AI's expansion in critical sectors has prompted regulatory efforts focusing on safe deployment. Current frameworks, based on procedural guidelines, assume scientific benchmarking validates AI safety. However, this overlooks AI's unique technical challenges. Effective regulation requires a causal theory linking test outcomes to future performance. A two-tiered framework is proposed: human oversight for high-risk applications and risk communication for lower-risk uses. This highlights the need for reconsidering AI regulation's foundational assumptions. Global interest in AI regulation is growing, focusing on balancing benefits and risks. Efforts by governments and international actors aim to guide AI development through regulatory standards. However, major initiatives neglect scientific benchmarking, using vague terminology. Addressing AI interpretability is crucial for successful regulation. The lack of understanding of AI models' decision-making processes hinders the ability to predict their behavior in unseen scenarios, undermining the foundation of ex-ante AI regulation.

Key findings

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Introduction
Background
Overview of AI's expansion in critical sectors
Current regulatory efforts focusing on AI safety
Objective
To propose a two-tiered framework for AI regulation
To highlight the need for reconsidering foundational assumptions in AI regulation
The Need for a Causal Theory in AI Regulation
Linking Test Outcomes to Future Performance
Importance of a causal theory in AI regulation
Challenges in applying procedural guidelines to AI safety
A Two-Tiered Framework for AI Regulation
Human Oversight for High-Risk Applications
Definition of high-risk applications
Role of human oversight in ensuring safety
Risk Communication for Lower-Risk Uses
Importance of risk communication
Tailoring regulation based on risk levels
Reconsidering Foundational Assumptions in AI Regulation
Current Regulatory Frameworks
Overview of existing frameworks
Limitations in addressing AI's unique technical challenges
The Role of Scientific Benchmarking
Importance of scientific benchmarking in AI regulation
Current gaps in regulatory approaches
Balancing Benefits and Risks in AI Regulation
Global Interest and Efforts
Growth in global interest in AI regulation
Government and international actor initiatives
Challenges in Balancing Benefits and Risks
Use of vague terminology in regulatory standards
Neglect of scientific benchmarking in major initiatives
Addressing AI Interpretability for Successful Regulation
Importance of AI Interpretability
Understanding AI models' decision-making processes
Predicting AI behavior in unseen scenarios
Challenges and Solutions
Current challenges in AI interpretability
Strategies for improving AI interpretability in regulation
Conclusion
Summary of Proposed Framework
Future Directions for AI Regulation
Importance of ongoing research and adaptation
Collaboration between governments, international actors, and AI experts
Basic info
papers
computation and language
machine learning
artificial intelligence
Advanced features
Insights
What is the significance of addressing AI interpretability in the context of successful regulation?
How does the proposed two-tiered framework address the risks associated with AI deployment?
What are the main challenges in regulating AI's expansion in critical sectors?
Why is a causal theory linking test outcomes to future AI performance important for regulation?