You Shouldn't Trust Me: Learning Models Which Conceal Unfairness From Multiple Explanation Methods
By Botty Dimanov, Umang Bhatt, Mateja Jamnik, Adrian Weller. 2020
110 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Health Informatics, Safety, Robustness & Interpretability, AI Ethics, Fairness & Society
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Mohit Bansal
- Percy Liang
- Yoshua Bengio
- Jianfeng Gao
- Luke S. Zettlemoyer
- Adrian Weller
- Noah A. Smith
- Yejin Choi
In Health Informatics
- Adrian Weller
- Zhi Chen
- Lesia Semenova
- Chudi Zhong
- Haiyang Huang
- Cynthia Rudin
- Octavio Loyola-González
- Timo Speith
In Safety, Robustness & Interpretability
- Adrian Weller
- Lesia Semenova
- Zhi Chen
- Chudi Zhong
- Haiyang Huang
- Cynthia Rudin
- Sara Hooker
- David Krueger
In AI Ethics, Fairness & Society
- Adrian Weller
- Lesia Semenova
- Chudi Zhong
- Zhi Chen
- Haiyang Huang
- Amanda Askell
- Allan Dafoe
- Peter Eckersley
Citations in the corpus
- Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges (2021, background): “, 2019), assessment of fairness and variable importance (Rudin et al., 2020; Dimanov et al., 2020), and uncertainty bands for variable importance (Gosiewska and Biecek, 2020; Fisher et al.”
- Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claims (2020, background): “, the reliability of existing methods is an active area of research [85] [86] [87] [88] [89] [90] [91] [92] [93].”