arXiv:2607.03425v1 Announce Type: new Abstract: Algorithmic recourse addresses the challenge of providing tailored recommendations to users affected by unfavorable machine learning decisions, in potentially high-stakes scenarios. Traditional approaches to recourse often rely on the closest counterfactual explanations or assume a priori knowledge of a user's causal structure, resulting in interventions that overlook individual contexts and specific feature interactions. To overcome these limitations, we study a human-in-the-loop framework that iteratively approximates the user's structural caus
The increasing deployment of machine learning in high-stakes decisions necessitates robust methods for explaining and correcting unfavorable outcomes, driving research into personalized recourse.
This research addresses a critical limitation in current AI ethical frameworks by focusing on individual user contexts for algorithmic recourse, which is crucial for trust and fairness in AI systems.
The shift from generic to personalized causal recourse, incorporating human feedback, will lead to more effective and equitable AI interventions, reducing the risk of discriminatory or irrelevant recommendations.
- · Individuals affected by AI decisions
- · AI ethics researchers
- · Companies deploying AI in critical sectors
- · Regulators
- · Generic counterfactual explanation approaches
- · AI systems lacking transparency
Machine learning models will become more aligned with individual user needs and specific causality.
Increased user trust in AI systems could accelerate adoption in sensitive domains such as finance and healthcare.
The human-in-the-loop framework could evolve into a general paradigm for continuously adaptive and ethical AI design.
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Read at arXiv cs.AI