Explaining Models: An Empirical Study of How Explanations Impact Fairness Judgment

Understand

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 people's fairness judgments of ML systems.