Open the Black Box Data-Driven Explanation of Black Box Decision Systems

TL;DR

This paper proposes a local-to-global framework for explaining black box decision systems using logic-based rules, enabling transparent, interpretable, and fair decision-making by auditing and generalizing local explanations.

Contribution

It introduces a novel local-to-global explanation framework that uses expressive logic rules, combining local instance explanations with global generalizations for black box models.

Findings

Early promising results demonstrate the effectiveness of the approach.

The framework enhances transparency and interpretability of black box systems.

It offers a new pathway for auditing and understanding complex decision models.

Abstract

Black box systems for automated decision making, often based on machine learning over (big) data, map a user's features into a class or a score without exposing the reasons why. This is problematic not only for lack of transparency, but also for possible biases hidden in the algorithms, due to human prejudices and collection artifacts hidden in the training data, which may lead to unfair or wrong decisions. We introduce the local-to-global framework for black box explanation, a novel approach with promising early results, which paves the road for a wide spectrum of future developments along three dimensions: (i) the language for expressing explanations in terms of highly expressive logic-based rules, with a statistical and causal interpretation; (ii) the inference of local explanations aimed at revealing the logic of the decision adopted for a specific instance by querying and auditing the black box in the vicinity of the target instance; (iii), the bottom-up generalization of the many local explanations into simple global ones, with algorithms that optimize the quality and comprehensibility of explanations.