# Minimalistic Explanations: Capturing the Essence of Decisions

arXiv: https://arxiv.org/abs/1905.02994 · HTML: https://mlsanity.com/paper/1905.02994/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 May 2019.

## Built on by (most cited first, 1 of 1)

- [How explainable AI affects human performance: A systematic review of the behavioural consequences of saliency maps](https://mlsanity.com/paper/2404.16042/) (2024)

## Abstract (arXiv)

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.