Transparency: Motivations and Challenges
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The explainable ML community has highlighted shortfalls including lack of consistent language and definitions, lack of clearly defined explainability goals and desiderata, and lack of consensus on metrics and methods for evaluating explanation quality.
“While this expansion of the field has yielded a rich body of methodological work, recently, the community has begun to highlight the shortfalls such as the lack of consistent language and definitions, the lack of clearly defined explainability goals, and desiderata; and the lack of consensus on metrics and methods of evaluating the quality of explanations (Doshi-Velez and Kim, 2017; Lipton, 2018;”
From Explainable machine learning for public policy: Use cases, gaps, and research directions (2023) · cites this paper as Weller, 2019
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- Authors
- Weller, A.
- Affiliations
- University of Cambridge (GB)
- Corresponding author
- Weller, Adrian
- ORCID
- Weller, Adrian
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2019
- Pages
- 23-40
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W2967578682
- Cited by
- 198
- References
- 62
- Citations by year
- 2026: 18 · 2025: 38 · 2024: 25 · 2023: 50 · 2022: 30 · 2021: 24 · 2020: 10 · 2019: 3
- FWCI
- 6.41
- Type
- conference paper
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Lecture notes in computer science (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Physical Sciences › Computer Science › Artificial Intelligence › Multi-Agent Systems and Negotiation
- Keywords
- transparency, intelligent systems, privacy, fairness, trust
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