The Impact of Explanations on Fairness in Human-AI Decision-Making: Protected vs Proxy Features

TL;DR

This study investigates how explanations and disclosures about protected and proxy features influence human perception of AI fairness and their ability to improve fairness in decision-making.

Contribution

It reveals that explanations aid in detecting direct biases but may increase bias agreement, while disclosures help mitigate this for indirect biases.

Findings

Explanations improve detection of direct biases.

Disclosures reduce bias agreement for proxy-based biases.

Combined approaches enhance fairness perception and decision-making.

Abstract

AI systems have been known to amplify biases in real-world data. Explanations may help human-AI teams address these biases for fairer decision-making. Typically, explanations focus on salient input features. If a model is biased against some protected group, explanations may include features that demonstrate this bias, but when biases are realized through proxy features, the relationship between this proxy feature and the protected one may be less clear to a human. In this work, we study the effect of the presence of protected and proxy features on participants' perception of model fairness and their ability to improve demographic parity over an AI alone. Further, we examine how different treatments -- explanations, model bias disclosure and proxy correlation disclosure -- affect fairness perception and parity. We find that explanations help people detect direct but not indirect biases. Additionally, regardless of bias type, explanations tend to increase agreement with model biases. Disclosures can help mitigate this effect for indirect biases, improving both unfairness recognition and decision-making fairness. We hope that our findings can help guide further research into advancing explanations in support of fair human-AI decision-making.