Reliability in Machine Learning

Grote, Thomas; Genin, Konstantin; Sullivan, Emily

doi:10.1111/phc3.12974

Philosophy Compass

2024

DOI: 10.1111/phc3.12974

|View full text |Cite

Reliability in Machine Learning

Thomas Grote,

Konstantin Genin,

Emily Sullivan

Abstract: Issues of reliability are claiming center‐stage in the epistemology of machine learning. This paper unifies different branches in the literature and points to promising research directions, whilst also providing an accessible introduction to key concepts in statistics and machine learning – as far as they are concerned with reliability.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article2

Relationship

Self Cite0

Independent2

Authors

Journals

Cited by 2 publications

References 82 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Deference to opaque systems and morally exemplary decisions

Fritz

2024

AI & Soc

View full text Add to dashboard Cite

Many have recently argued that there are weighty reasons against making high-stakes decisions solely on the basis of recommendations from artificially intelligent (AI) systems. Even if deference to a given AI system were known to reliably result in the right action being taken, the argument goes, that deference would lack morally important characteristics: the resulting decisions would not, for instance, be based on an appreciation of right-making reasons. Nor would they be performed from moral virtue; nor would they have moral worth. I argue that, even if these characteristics all have intrinsic value, that intrinsic value has no practical relevance to decisions about whether to defer to AI. I make that point by drawing on a lesson from the literature on moral testimony. Once it is granted that deference to a reliable source is the policy most likely to bring about right action, a refusal to defer carries with it a heightened risk of wronging and mistreating people. And that heightened risk of wrongdoing, I argue, cannot be justified by appeal to the intrinsic value of striving for a morally exemplary decision-making process.

show abstract

Deference to opaque systems and morally exemplary decisions

Fritz

2024

AI & Soc

View full text Add to dashboard Cite

show abstract

Foundation models in healthcare require rethinking reliability

Grote,

Freiesleben,

Berens

2024

Nat Mach Intell

View full text Add to dashboard Cite

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Reliability in Machine Learning

Cited by 2 publications

References 82 publications

Deference to opaque systems and morally exemplary decisions

Deference to opaque systems and morally exemplary decisions

Foundation models in healthcare require rethinking reliability

Contact Info

Product

Resources

About