Minimalistic Explanations: Capturing the Essence of Decisions

TL;DR

This paper investigates minimalistic post-hoc explanations for neural network image classifications, showing they can effectively convey decision essence but have limited context, and human explanations increase trust.

Contribution

It introduces a minimalistic explanation approach and provides initial insights into their effectiveness and trustworthiness compared to human explanations.

Findings

Minimalistic explanations improve object identification accuracy.

Human explanations increase trust ratings by 79%.

Explanation effectiveness varies with decision correctness.

Abstract

The use of complex machine learning models can make systems opaque to users. Machine learning research proposes the use of post-hoc explanations. However, it is unclear if they give users insights into otherwise uninterpretable models. One minimalistic way of explaining image classifications by a deep neural network is to show only the areas that were decisive for the assignment of a label. In a pilot study, 20 participants looked at 14 of such explanations generated either by a human or the LIME algorithm. For explanations of correct decisions, they identified the explained object with significantly higher accuracy (75.64% vs. 18.52%). We argue that this shows that explanations can be very minimalistic while retaining the essence of a decision, but the decision-making contexts that can be conveyed in this manner is limited. Finally, we found that explanations are unique to the explainer and human-generated explanations were assigned 79% higher trust ratings. As a starting point for further studies, this work shares our first insights into quality criteria of post-hoc explanations.