Locally Pareto-Optimal Interpretations for Black-Box Machine Learning Models

• URL: http://arxiv.org/abs/2508.15220v1
• Date: Thu, 21 Aug 2025 04:11:20 GMT
• Title: Locally Pareto-Optimal Interpretations for Black-Box Machine Learning Models
• Abstract summary: We develop a framework based on local optimality guarantees to enable scalable synthesis of interpretations.<n>We show that our approach yields interpretations that closely match those synthesized by methods offering global guarantees.
• Score: 0.8906214436849201
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Creating meaningful interpretations for black-box machine learning models involves balancing two often conflicting objectives: accuracy and explainability. Exploring the trade-off between these objectives is essential for developing trustworthy interpretations. While many techniques for multi-objective interpretation synthesis have been developed, they typically lack formal guarantees on the Pareto-optimality of the results. Methods that do provide such guarantees, on the other hand, often face severe scalability limitations when exploring the Pareto-optimal space. To address this, we develop a framework based on local optimality guarantees that enables more scalable synthesis of interpretations. Specifically, we consider the problem of synthesizing a set of Pareto-optimal interpretations with local optimality guarantees, within the immediate neighborhood of each solution. Our approach begins with a multi-objective learning or search technique, such as Multi-Objective Monte Carlo Tree Search, to generate a best-effort set of Pareto-optimal candidates with respect to accuracy and explainability. We then verify local optimality for each candidate as a Boolean satisfiability problem, which we solve using a SAT solver. We demonstrate the efficacy of our approach on a set of benchmarks, comparing it against previous methods for exploring the Pareto-optimal front of interpretations. In particular, we show that our approach yields interpretations that closely match those synthesized by methods offering global guarantees.

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