Are contrastive explanations useful?

James Forrest, Somayajulu Sripada, Wei Pang, George M. Coghill

Research output: Contribution to conferenceUnpublished paperpeer-review

1 Citation (Scopus)
36 Downloads (Pure)


From the user perspective (data subjects and data controllers), useful explanations of ML decisions are selective, contrastive and social. In this paper, we describe an algorithm for generating selective and contrastive explanations and experimentally study its usefulness to users.

Original languageEnglish
Number of pages8
Publication statusPublished - 1 Jun 2021
Event2021 SICSA eXplainable Artifical Intelligence Workshop, SICSA XAI 2021 - Aberdeen, United Kingdom
Duration: 1 Jun 20211 Jun 2021


Conference2021 SICSA eXplainable Artifical Intelligence Workshop, SICSA XAI 2021
Country/TerritoryUnited Kingdom

Bibliographical note

Funding Information:
Supported by EPSRC DTP Grant Number EP/N509814/1


  • Contrastive explanations
  • Interpretable ML
  • XAI


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