Should We Trust Algorithms?
By David J. Spiegelhalter. 2020
59 citations recorded by the source corpus.
There is increasing use of algorithms in the health care and criminal justice systems, and corresponding increased concern with their ethical use. But perhaps a more basic issue is whether we should believe what we hear about them and what the algorithm tells us. It is illuminating to distinguish between the trustworthiness of claims made about an algorithm, and those made by an algorithm, which reveals the potential contribution of statistical science to both evaluation and ‘intelligent transparency.’ In particular, a four-phase evaluation structure is proposed, parallel to that adopted for pharmaceuticals.Keywords: trustworthiness, evaluation, AI, criminal justice system, medical AI, transparency.
Subfields: Artificial Intelligence, Health Informatics, Public Health, Environmental and Occupational Health, AI Ethics, Fairness & Society, Philosophy
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
- Lesia Semenova
- Chudi Zhong
- Zhi Chen
- Haiyang Huang
- Cynthia Rudin
- Tong Wang
- Octavio Loyola-González
- Berk Ustun
In Health Informatics
- Zhi Chen
- Lesia Semenova
- Chudi Zhong
- Haiyang Huang
- Cynthia Rudin
- Octavio Loyola-González
- Timo Speith
- S. Lo Piano
In Public Health, Environmental and Occupational Health
In AI Ethics, Fairness & Society
- Lesia Semenova
- Chudi Zhong
- Zhi Chen
- Haiyang Huang
- Cynthia Rudin
- Octavio Loyola-González
- Adrian Weller
- Yuval Pinter
Citations in the corpus
- Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges (2021, background): “It is universally agreed that interpretability is a key element of trust for AI models (Wagstaff, 2012; Rudin and Wagstaff, 2014; Lo Piano, 2020; Ashoori and Weisz, 2019; Thiebes et al., 2020; Spiegelhalter, 2020; Brundage et al., 2020).”
- Trust in AI: progress, challenges, and future directions (2024, unknown): “Distrust of arti fi cial intelligence, in this domain, can range from concern over whether the company or government using the algorithm is trustworthy in what they say about its use to concern over what is said by the algorithm and why it came to that recommendation (Spiegelhalter, 2020).”