ReVise: A Human-AI Interface for Incremental Algorithmic Recourse
Kaustav Bhattacharjee, Jun Yuan, and Aritra Dasgupta

TL;DR
ReVise introduces an interactive visual interface that guides individuals through incremental algorithmic recourse steps, improving understanding and decision-making in AI-driven outcomes.
Contribution
The paper presents a novel visual analytic workflow and interface for incremental recourse planning, addressing the gap in existing methods that focus only on final outcomes.
Findings
Participants found the tool helpful for understanding recourse steps
The interface enabled better navigation of recourse options
Subjective feedback indicated improved decision confidence
Abstract
The recent adoption of artificial intelligence in socio-technical systems raises concerns about the black-box nature of the resulting decisions in fields such as hiring, finance, admissions, etc. If data subjects -- such as job applicants, loan applicants, and students -- receive an unfavorable outcome, they may be interested in algorithmic recourse, which involves updating certain features to yield a more favorable result when re-evaluated by algorithmic decision-making. Unfortunately, when individuals do not fully understand the incremental steps needed to change their circumstances, they risk following misguided paths that can lead to significant, long-term adverse consequences. Existing recourse approaches focus exclusively on the final recourse goal but neglect the possible incremental steps to reach the goal with real-life constraints, user preferences, and model artifacts. To…
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Taxonomy
TopicsData Visualization and Analytics · Explainable Artificial Intelligence (XAI) · Ethics and Social Impacts of AI
