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Explaining the relations between key concepts in XAI (that were not mentioned in the text), including: Interpretability, Model Transparency, Simulatability, Decomposability, Algorithmic Transparency, Model Functionality, Visualization, and Local Explanations |
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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>
[[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. A model is transparent "if the processes that extract model parameters from training data and generate labels from testing data can be described and motivated by the approach designer."<ref name=":4">{{Cite journal|last1=Roscher|first1=R.|last2=Bohn|first2=B.|last3=Duarte|first3=M. F.|last4=Garcke|first4=J.|date=2020|title=Explainable Machine Learning for Scientific Insights and Discoveries|journal=IEEE Access|volume=8|pages=42200–42216|doi=10.1109/ACCESS.2020.2976199|arxiv=1905.08883 |bibcode=2020IEEEA...842200R |issn=2169-3536|doi-access=free}}</ref> Interpretability describes the possibility of comprehending the ML model and presenting the underlying basis for decision-making in a way that is understandable to humans.<ref name="Interpretable machine learning: def">{{cite journal|last1=Murdoch|first1=W. James|last2=Singh|first2=Chandan|last3=Kumbier|first3=Karl|last4=Abbasi-Asl|first4=Reza|last5=Yu|first5=Bin|date=2019-01-14|title=Interpretable machine learning: definitions, methods, and applications|journal=Proceedings of the National Academy of Sciences of the United States of America|volume=116|issue=44|pages=22071–22080|arxiv=1901.04592|doi=10.1073/pnas.1900654116|pmid=31619572|pmc=6825274|bibcode= |doi-access=free}}</ref><ref name="Lipton 31–57">{{Cite journal|last=Lipton|first=Zachary C.|date=June 2018|title=The Mythos of Model Interpretability: In machine learning, the concept of interpretability is both important and slippery.|journal=Queue|language=en|volume=16|issue=3|pages=31–57|doi=10.1145/3236386.3241340|issn=1542-7730|doi-access=free}}</ref><ref>{{Cite web|date=2019-10-22|title=Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI|url=https://deepai.org/publication/explainable-artificial-intelligence-xai-concepts-taxonomies-opportunities-and-challenges-toward-responsible-ai|access-date=2021-01-13|website=DeepAI}}</ref> Explainability is a concept that is recognized as important, but a consensus definition is not yet available;<ref name=":4" /> one possibility is "the collection of features of the interpretable ___domain that have contributed, for a given example, to producing a decision (e.g., classification or regression)"
In summary, Interpretability refers to the user's ability to understand model outputs, while Model Transparency includes Simulatability (reproducibility of predictions), Decomposability (intuitive explanations for parameters), and Algorithmic Transparency (explaining how algorithms work). Model Functionality focuses on textual descriptions, visualization, and local explanations, which clarify specific outputs or instances rather than entire models. All these concepts aim to enhance the comprehensibility and usability of AI systems <ref name="NCB23">{{Cite web|author = Notovich, Aviv., Chalutz-Ben Gal, Hila, & Ben-Gal, Irad (2023). |format = PDF|title = Explainable Artificial Intelligence (XAI): Motivation, Terminology, and Taxonomy.|url = https://www.iradbengal.sites.tau.ac.il/_files/ugd/901879_fcdc9f3f44dc41968ba476a66f60f078.pdf|publisher = In Machine Learning for Data Science Handbook: Data Mining and Knowledge Discovery Handbook (pp. 971-985). Cham: Springer International Publishing.}}</ref>.
If algorithms fulfill these principles, they provide a basis for justifying decisions, tracking them and thereby verifying them, improving the algorithms, and exploring new facts.<ref>{{Cite journal|last1=Adadi|first1=A.|last2=Berrada|first2=M.|date=2018|title=Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)|journal=IEEE Access|volume=6|pages=52138–52160|doi=10.1109/ACCESS.2018.2870052|bibcode=2018IEEEA...652138A |issn=2169-3536|doi-access=free}}</ref>
Sometimes it is also possible to achieve a high-accuracy result with white-box ML algorithms. These algorithms have an interpretable structure that can be used to explain predictions.<ref name=":6">{{Cite journal|last=Rudin|first=Cynthia|date=2019|title=Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead|journal=Nature Machine Intelligence|language=en|volume=1|issue=5|pages=206–215|doi=10.1038/s42256-019-0048-x|pmid=35603010 |pmc=9122117 |arxiv=1811.10154|issn=2522-5839|doi-access=free}}</ref> Concept Bottleneck Models, which use concept-level abstractions to explain model reasoning, are examples of this and can be applied in both image<ref name="Koh Nguyen Tang Mussmann Pierson Kim Liang 2020">{{Cite conference|last1=Koh|first1=P. W.|last2=Nguyen|first2=T.|last3=Tang|first3=Y. S.|last4=Mussmann|first4=S.|last5=Pierson|first5=E.|last6=Kim|first6=B.|last7=Liang|first7=P.|date=November 2020|title=Concept bottleneck models|book-title=International Conference on Machine Learning|pages=5338–5348|publisher=PMLR}}</ref> and text<ref name="Ludan Lyu Yang Dugan Yatskar Callison-Burch 2023">{{Cite arXiv|last1=Ludan|first1=J. M.|last2=Lyu|first2=Q.|last3=Yang|first3=Y.|last4=Dugan|first4=L.|last5=Yatskar|first5=M.|last6=Callison-Burch|first6=C.|date=2023|title=Interpretable-by-Design Text Classification with Iteratively Generated Concept Bottleneck|class=cs.CL |eprint=2310.19660}}</ref> prediction tasks. This is especially important in domains like medicine, defense, finance, and law, where it is crucial to understand decisions and build trust in the algorithms.<ref name=":3" /> Many researchers argue that, at least for supervised machine learning, the way forward is symbolic regression, where the algorithm searches the space of mathematical expressions to find the model that best fits a given dataset.<ref name="Wenninger Kaymakci Wiethe 2022 p=118300">{{cite journal | last1=Wenninger | first1=Simon | last2=Kaymakci | first2=Can | last3=Wiethe | first3=Christian | title=Explainable long-term building energy consumption prediction using QLattice | journal=Applied Energy | publisher=Elsevier BV | volume=308 | year=2022 | issn=0306-2619 | doi=10.1016/j.apenergy.2021.118300 | page=118300| bibcode=2022ApEn..30818300W | s2cid=245428233 }}</ref><ref name="Christiansen Wilstrup Hedley 2022 p.">{{cite journal | last1=Christiansen | first1=Michael | last2=Wilstrup | first2=Casper | last3=Hedley | first3=Paula L. | title=Explainable "white-box" machine learning is the way forward in preeclampsia screening | journal=American Journal of Obstetrics and Gynecology | publisher=Elsevier BV | year=2022 | volume=227 | issue=5 | issn=0002-9378 | doi=10.1016/j.ajog.2022.06.057 | page=791| pmid=35779588 | s2cid=250160871 }}</ref><ref name="Wilstup Cave p.">{{citation | last1=Wilstup | first1=Casper | last2=Cave | first2=Chris | title=Combining symbolic regression with the Cox proportional hazards model improves prediction of heart failure deaths | publisher=Cold Spring Harbor Laboratory | date=2021-01-15 | doi=10.1101/2021.01.15.21249874 | page=| s2cid=231609904 }}</ref>
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