Are Contrastive Explanations Useful?

James Forrest, Somayajulu Sripada, Wei Pang, George Coghill

Research output: Contribution to journalConference articlepeer-review

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Abstract

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
Pages (from-to)9-16
Number of pages8
JournalCEUR Workshop Proceedings
Volume2894
Publication statusPublished - 2 Jul 2021
EventSICSA Workshop on eXplainable Artificial Intelligence 2021 - Aberdeen, United Kingdom
Duration: 1 Jun 20211 Jun 2021

Keywords

  • Contrastive explanations
  • Interpretable ML
  • XAI

ASJC Scopus subject areas

  • Computer Science(all)

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