Explainable artificial intelligence: Difference between revisions

Revision as of 02:56, 27 June 2025

Revision as of 02:57, 27 June 2025

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XAI hopes to help users of AI-powered systems perform more effectively by improving their understanding of how those systems reason.<ref>{{Cite journal|last=Alizadeh|first=Fatemeh|date=2021|title=I Don't Know, Is AI Also Used in Airbags?: An Empirical Study of Folk Concepts and People's Expectations of Current and Future Artificial Intelligence|url=https://www.researchgate.net/publication/352638184|journal=Icom|volume=20 |issue=1 |pages=3–17 |doi=10.1515/icom-2021-0009|s2cid=233328352}}</ref> XAI may be an implementation of the social [[right to explanation]].<ref name=":0">{{Cite journal|last1=Edwards|first1=Lilian|last2=Veale|first2=Michael|date=2017|title=Slave to the Algorithm? Why a 'Right to an Explanation' Is Probably Not the Remedy You Are Looking For|journal=Duke Law and Technology Review|volume=16|pages=18|ssrn=2972855}}</ref> Even if there is no such legal right or regulatory requirement, XAI can improve the [[user experience]] of a product or service by helping end users trust that the AI is making good decisions.<ref>{{Cite web |last=Do Couto |first=Mark |date=February 22, 2024 |title=Entering the Age of Explainable AI |url=https://tdwi.org/Articles/2024/02/22/ADV-ALL-Entering-the-Age-of-Explainable-AI.aspx |access-date=2024-09-11 |website=TDWI}}</ref> XAI aims to explain what has been done, what is being done, and what will be done next, and to unveil which information these actions are based on.<ref name=":3">{{Cite journal|last1=Gunning|first1=D.|last2=Stefik|first2=M.|last3=Choi|first3=J.|last4=Miller|first4=T.|last5=Stumpf|first5=S.|last6=Yang|first6=G.-Z.|date=2019-12-18|title=XAI-Explainable artificial intelligence|url=https://openaccess.city.ac.uk/id/eprint/23405/|journal=Science Robotics|language=en|volume=4|issue=37|pages=eaay7120|doi=10.1126/scirobotics.aay7120|pmid=33137719|issn=2470-9476|doi-access=free}}</ref> This makes it possible to confirm existing knowledge, challenge existing knowledge, and generate new assumptions.<ref>{{Cite journal|last1=Rieg|first1=Thilo|last2=Frick|first2=Janek|last3=Baumgartl|first3=Hermann|last4=Buettner|first4=Ricardo|date=2020-12-17|title=Demonstration of the potential of white-box machine learning approaches to gain insights from cardiovascular disease electrocardiograms|journal=PLOS ONE|language=en|volume=15|issue=12|pages=e0243615|doi=10.1371/journal.pone.0243615|issn=1932-6203|pmc=7746264|pmid=33332440|bibcode=2020PLoSO..1543615R|doi-access=free}}</ref>

==Background==

[[Machine learning]] (ML) algorithms used in AI can be categorized as [[White-box testing|white-box]] or [[Black box|black-box]].<ref>{{Cite journal|last1=Vilone|first1=Giulia|last2=Longo|first2=Luca|title= Classification of Explainable Artificial Intelligence Methods through Their Output Formats |journal=Machine Learning and Knowledge Extraction|year=2021|volume=3|issue=3|pages=615–661|doi=10.3390/make3030032|doi-access=free }}</ref> White-box models provide results that are understandable to experts in the ___domain. Black-box models, on the other hand, are extremely hard to explain and may not be understood even by ___domain experts.<ref>{{Cite journal|last=Loyola-González|first=O.|date=2019|title=Black-Box vs. White-Box: Understanding Their Advantages and Weaknesses From a Practical Point of View|journal=IEEE Access|volume=7|pages=154096–154113|doi=10.1109/ACCESS.2019.2949286|bibcode=2019IEEEA...7o4096L |issn=2169-3536|doi-access=free}}</ref> XAI algorithms follow the three principles of transparency, interpretability, and explainability.