Explaining models: an empirical study of how explanations impact fairness judgment

Abstract Ensuring fairness of machine learning systems is a human-in-the-loop process. It relies on developers, users, and the general public to identify fairness problems and make improvements. To facilitate the process we need effective, unbiased, and user-friendly explanations that people can confidently rely on. Towards that end, we conducted an empirical study with four types of programmatically generated explanations to understand how they impact peoples fairness judgments of ML systems. With an experiment involving more than 160 Mechanical Turk workers, we show that: 1) Certain explanations are considered inherently less fair, while others can enhance peoples confidence in the fairness of the algorithm; 2) Different fairness problems-such as model-wide fairness issues versus case-specific fairness discrepancies-may be more effectively exposed through different styles of explanation; 3) Individual differences, including prior positions and judgment criteria of algorithmic fairness, impact how people react to different styles of explanation. We conclude with a discussion on providing personalized and adaptive explanations to support fairness judgments of ML systems.
  • Jonathan Dodge
  • Q. Vera Liao (IBM US)
  • Yunfeng Zhang (IBM US)
  • Rachel Bellamy (IBM US)
  • Casey Dugan (IBM US)
Date Mar-2019
Venue 24th International Conference on Intelligent User Interfaces