Has machine learning rendered simple rules obsolete?

Area of Science:

• Legal Studies
• Computer Science
• Economics

Background:

• Richard Epstein argued for simple legal rules over complex regulations.
• The advent of artificial intelligence, particularly machine learning (ML), raises questions about the continued relevance of simple legal rules.

Purpose of the Study:

• To evaluate whether machine learning (ML) can create superior algorithmic rules compared to traditional simple legal rules.
• To assess the impact of ML on Epstein's argument for simple legal rules in complex environments.

Main Methods:

• The paper presents a theoretical argument against ML's efficacy in generating legal rules.
• It identifies and analyzes three key limitations ML faces: data availability, the Lucas critique, and incentive compatibility.

Main Results:

• Machine learning (ML) faces significant challenges in developing effective legal or quasi-legal algorithmic rules.
• Barriers include insufficient data availability, the Lucas critique's implications for model reliability, and difficulties in eliciting truthful information through incentive mechanisms.

Conclusions:

• The case for simple legal rules remains robust despite advancements in machine learning (ML).
• ML's inherent limitations prevent it from surpassing the effectiveness of simple legal rules in the legal domain.