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 |
|---|---|
| Pages (from-to) | 9-16 |
| Number of pages | 8 |
| Journal | CEUR Workshop Proceedings |
| Volume | 2894 |
| Publication status | Published - 2 Jul 2021 |
| Event | SICSA Workshop on eXplainable Artificial Intelligence 2021 - Aberdeen, United Kingdom Duration: 1 Jun 2021 → 1 Jun 2021 |
Keywords
- Contrastive explanations
- Interpretable ML
- XAI
ASJC Scopus subject areas
- General Computer Science