Psychological Benefits and Costs of Diversifying Algorithmic Recourse

TL;DR

This study investigates how diversifying algorithmic recourse affects psychological benefits and costs, revealing benefits for small sets but increased cognitive load for larger ones, highlighting the need for human-aware diversification methods.

Contribution

It provides empirical evidence on the psychological impacts of recourse diversification and emphasizes designing human-centric methods to balance benefits and costs.

Findings

Diversification improves willingness to act for small recourse sets.

Large sets with diverse recourse increase cognitive load and negative emotions.

Naive diversification can burden decision subjects, requiring better methods.

Abstract

Algorithmic recourse provides counterfactual action plans that help people overturn unfavorable AI decisions. While diverse recourse sets may improve transparency and motivation, they may also impose cognitive load and negative emotions by increasing counterfactual reasoning demands. To examine this trade-off, we conducted a between-subjects controlled experiment (N=750) that manipulated recourse-set diversity and size, and evaluated these effects on psychological benefits and costs. Results show that diversification enhances psychological benefits (e.g., willingness to act) for small sets without incurring additional psychological costs, whereas for large sets, it makes cognitive load more salient. These findings suggest that naively diversifying recourse can burden decision subjects, underscoring the need for new diversification methods that incorporate human cognition and psychology to mitigate such costs.