When Interpretability Is Unequally Distributed: Fairness in Hybrid Interpretable Models

2026-05-27 · Source: Takara TLDR - Daily AI Papers · Field: Technology & Digital — Artificial Intelligence & Machine Learning, AI Ethics & Interpretability · Depth: Expert, quick

Summary

A study by Ziba Jabbar Zare, Ulrich Aïvodji, Julien Ferry, and Thibaut Vidal introduces Interpretability Coverage Disparity (ICD) as a critical fairness concern in hybrid interpretable models. These models route some data examples to a transparent component and others to a black-box model, potentially leading to unequal interpretability distribution across demographic groups. ICD formalizes this issue as a demographic-parity-style measure for routing decisions. The researchers investigated ICD across four hybrid interpretable learning methods, three standard fairness benchmark datasets, and multiple sensitive attributes. Their experiments revealed substantial ICD in intermediate transparency regimes where both model components are active. They further demonstrated that applying simple coverage-disparity constraints can significantly reduce ICD in exact hybrid learning methods, with only marginal effects on accuracy and sparsity. Notably, mitigating ICD also improved standard algorithmic fairness metrics, highlighting the need to audit hybrid models for both predictive fairness and interpretability allocation.

Key takeaway

For Machine Learning Engineers designing or deploying hybrid interpretable models, you must extend your fairness audits beyond predictive accuracy to include Interpretability Coverage Disparity (ICD). Systematically evaluate how your model routes different demographic groups to transparent versus black-box components. Implementing simple coverage-disparity constraints can significantly reduce ICD with minimal impact on model performance, potentially improving overall algorithmic fairness. Prioritize equitable interpretability access for all user groups.

Key insights

Hybrid interpretable models can unequally route demographic groups to black-box components, necessitating auditing for Interpretability Coverage Disparity (ICD) alongside predictive fairness.

Principles

• Hybrid models introduce procedural interpretability fairness concerns.
• Interpretability allocation must be audited for disparity.
• Mitigating ICD can enhance standard algorithmic fairness.

Method

The study formalizes Interpretability Coverage Disparity (ICD) as a demographic-parity-style measure for routing decisions. It then uses predictive multiplicity tools to analyze ICD and applies simple coverage-disparity constraints to mitigate it in exact hybrid learning methods.

In practice

• Audit hybrid models for Interpretability Coverage Disparity (ICD).
• Apply coverage-disparity constraints in exact hybrid learning.
• Integrate ICD into algorithmic fairness metric evaluations.

Topics

• Hybrid Interpretable Models
• Interpretability Coverage Disparity
• Algorithmic Fairness
• Model Auditing
• Explainable AI
• Demographic Parity

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, AI Ethicist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Takara TLDR - Daily AI Papers.