Psychological Benefits and Costs of Diversifying Algorithmic Recourse
Tomu Tominaga, Naomi Yamashita, Takeshi Kurashima
TLDR
Diversifying algorithmic recourse benefits users with small action sets but increases cognitive load with large sets, highlighting the need for human-centered design.
Key contributions
- Diversifying algorithmic recourse improves user benefits for small action sets.
- However, for large recourse sets, diversification significantly increases cognitive load.
- An N=750 experiment revealed this trade-off between diversity, set size, and user experience.
- Highlights the need for human-cognition-aware diversification methods in AI recourse.
Why it matters
This paper reveals a critical trade-off in designing fair AI systems, showing that simply diversifying recourse options can backfire by burdening users. Its findings are crucial for developing user-friendly and psychologically sound algorithmic recourse mechanisms that truly empower individuals.
Original 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.
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