Explainability in Mechanism Design: Recent Advances and the Road Ahead

Explainability in AI extends beyond machine learning: While most attention has focused on explaining "black-box" algorithms like neural networks, the paper highlights that explaining decisions in social choice and mechanism design systems is equally important and presents distinct challenges that require different approaches.

Economic systems require different explainability standards: Mechanism design deals with systems where multiple agents have conflicting interests and "no single choice maximizes all individual utility functions." This means explanations must account for trade-offs between competing interests, making them fundamentally different from explanations in other AI domains.

Explainability is critical for user trust in complex systems: The paper positions explainability as "the next step towards increasing user trust in, acceptance of and reliance on AI systems," suggesting that without clear explanations of how decisions are made, users may resist or distrust AI-driven systems—particularly those affecting economic or social outcomes.

Mechanism design explainability faces unique challenges: The paper identifies specific challenges when explaining mechanism design decisions that differ from general Explainable AI challenges, though these require careful study to understand how economic incentives and multi-agent interactions complicate the explanation process.

Solutions exist but require tailored approaches: The authors propose "solution concepts" to address explainability challenges in mechanism design, indicating that generic explainability methods from machine learning cannot simply be transferred to economic and social choice systems.