Rethinking Algorithmic Fairness for Human-AI Collaboration

Understand

Existing approaches to algorithmic fairness aim to ensure equitable outcomes if human decision-makers comply perfectly with algorithmic decisions.

• However, perfect compliance with the algorithm is rarely a reality or even a desirable outcome in human-AI collaboration.
• Yet, recent studies have shown that selective compliance with fair algorithms can amplify discrimination relative to the prior human policy.
• As a consequence, ensuring equitable outcomes requires fundamentally different algorithmic design principles that ensure robustness to the decision-maker's (a priori unknown) compliance pattern.