arXiv:2605.28626v1 Announce Type: new Abstract: Hybrid interpretable models combine a transparent component with a black-box model by assigning some examples to the former and deferring the rest to the latter. While this design enables flexible tradeoffs between accuracy and interpretability, it also raises a distinct procedural fairness concern: some demographic groups may systematically receive interpretable decisions, while others are disproportionately routed to a black box. We formalize this issue as Interpretability Coverage Disparity (ICD), a demographic-parity-style measure applied to
The proliferation of hybrid AI models necessitates a deeper examination of fairness beyond traditional accuracy metrics, particularly as these systems are deployed in sensitive applications.
This research highlights a critical ethical and regulatory challenge in AI interpretability, influencing how fairness is defined and measured in deployed AI systems.
The concept of 'Interpretability Coverage Disparity' introduces a new facet of AI fairness, shifting the focus beyond outcomes to the procedural transparency afforded to different groups.
- · Ethical AI researchers
- · Regulatory bodies
- · AI audit firms
- · Public interest groups
- · AI developers ignoring fairness-in-interpretability
- · Organizations deploying opaque AI systems
- · Users disproportionately routed to black-box models
Increased scrutiny and demand for equitable interpretability in hybrid AI systems across industries.
Development of new metrics and frameworks for evaluating and mitigating interpretability coverage disparity in AI models.
Potential for new legislation or industry standards mandating transparent and equitably interpretable AI, particularly in high-stakes domains.
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Read at arXiv cs.LG