  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.
