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 language | English |
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| Pages | 9-16 |
| Number of pages | 8 |
| Publication status | Published - 1 Jun 2021 |
| Event | 2021 SICSA eXplainable Artifical Intelligence Workshop, SICSA XAI 2021 - Aberdeen, United Kingdom Duration: 1 Jun 2021 → … |
Conference
| Conference | 2021 SICSA eXplainable Artifical Intelligence Workshop, SICSA XAI 2021 |
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| Country/Territory | United Kingdom |
| City | Aberdeen |
| Period | 1/06/21 → … |
Keywords
- Contrastive explanations
- Interpretable ML
- XAI