Generating Simple, Conservative and Unifying Explanations for Logistic Regression Models

Sameen Maruf, Ingrid Zukerman, Xuelin Situ, Cécile Paris, Gholamreza Haffari

Abstract: In this paper, we generate and compare three types of explanations of Machine Learning (ML) predictions: simple, conservative and unifying. Simple explanations are concise, conservative explanations address the surprisingness of a prediction, and unifying explanations convey the extent to which an ML model’s predictions are applicable. The results of our user study show that (1) conservative and unifying explanations are liked equally and considered largely equivalent in terms of completeness, helpfulness for understanding the AI, and enticement to act, and both are deemed better than simple explanations; and (2)users’ views about explanations are influenced by the (dis)agreement between the ML model’s predictions and users’ estimations of these predictions, and by the inclusion/omission of features users expect to see in explanations.

External IDs:dblp:conf/inlg/MarufZSPH24