arXiv:2605.31272v1 Announce Type: new Abstract: As predictive models are increasingly deployed in high-stakes settings such as credit approval, there is a growing need for post-hoc methods that provide recourse to affected individuals. Many such models operate on tabular data, where features correspond to real-world attributes. Recently, in-context learning (ICL) has enabled large language models to perform tabular prediction by conditioning on labeled examples at inference time, without explicit training. However, algorithmic recourse for tabular decision-making under ICL remains largely unex
As AI models become ubiquitous in high-stakes decisions, the demand for accountability and explainability is accelerating, leading to focused research on recourse mechanisms.
Ensuring fairness and recourse in AI-driven decision-making is critical for public trust, regulatory compliance, and responsible deployment in sensitive sectors like finance.
This research introduces methods for providing actionable recourse for individuals affected by in-context learning models, making these powerful AI systems more interpretable and rectifiable.
- · Individuals denied services by AI
- · Financial institutions (regulatory compliance)
- · AI ethics researchers
- · Generative AI platforms
- · Developers of opaque AI models
- · Companies ignoring AI fairness
- · AI systems lacking interpretability
Increased adoption of ICL-based models in high-stakes fields due to improved recourse mechanisms.
New regulatory frameworks may emerge to mandate recourse capabilities for AI systems.
Enhanced public trust in AI could accelerate its integration into daily societal functions, potentially reducing human oversight in routine decisions.
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Read at arXiv cs.LG