Explainable artificial intelligence: Difference between revisions

Revision as of 16:29, 8 November 2024

Revision as of 07:23, 9 November 2024

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== Regulation ==

As regulators, official bodies, and general users come to depend on AI-based dynamic systems, clearer accountability will be required for [[automated decision-making]] processes to ensure trust and transparency. The first global conference exclusively dedicated to this emerging discipline was the 2017 [[International Joint Conference on Artificial Intelligence]]: Workshop on Explainable Artificial Intelligence (XAI).<ref>{{cite web|title=IJCAI 2017 Workshop on Explainable Artificial Intelligence (XAI)|url=http://www.intelligentrobots.org/files/IJCAI2017/IJCAI-17_XAI_WS_Proceedings.pdf|website=Earthlink|publisher=IJCAI|access-date=17 July 2017|archive-date=4 April 2019|archive-url=https://web.archive.org/web/20190404131609/http://www.intelligentrobots.org/files/IJCAI2017/IJCAI-17_XAI_WS_Proceedings.pdf|url-status=dead}}</ref> It has evolved over the years, with various workshops organised and co-located to many other international conferences, and it has now a dedicated global event, "The world conference on eXplainable Artificial Intelligence (see external link), with its own proceedings.<ref name="XAI-2023">{{cite book |author=<!--Not stated--> |date= 2023| title= Explainable Artificial Intelligence, First World Conference, xAI 2023, Lisbon, Portugal, July 26–28, 2023, Proceedings, Parts I/II/III |url= https://link.springer.com/book/10.1007/978-3-031-44070-0 |publisher=springer |isbn=978-3-031-44070-0}}</ref><ref name="XAI-2024">{{cite book |author=<!--Not stated-->|date= 2024| title= Explainable Artificial Intelligence, Second World Conference, xAI 2024, Valletta, Malta, July 17–19, 2024, Proceedings, Part I/II/III/IV |url=https://link.springer.com/book/10.1007/978-3-031-63787-2 |publisher=springer |isbn=978-3-031-63787-2}}</ref>

<ref name="XAI-2023">{{cite book |author=<!--Not stated--> |date= 2023| title= Explainable Artificial Intelligence, First World Conference, xAI 2023, Lisbon, Portugal, July 26–28, 2023, Proceedings, Parts I/II/III |url= https://link.springer.com/book/10.1007/978-3-031-44070-0 |publisher=springer |isbn=978-3-031-44070-0}}</ref>

<ref name="XAI-2024">{{cite book |author=<!--Not stated-->|date= 2024| title= Explainable Artificial Intelligence, Second World Conference, xAI 2024, Valletta, Malta, July 17–19, 2024, Proceedings, Part I/II/III/IV |url=https://link.springer.com/book/10.1007/978-3-031-63787-2 |publisher=springer |isbn=978-3-031-63787-2}}</ref>

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* {{ Cite journal | title= Random Forest similarity maps: A Scalable Visual Representation for Global and Local Interpretation| year= 2021| doi= 10.3390/electronics10222862| doi-access= free| last1= Mazumdar| first1= Dipankar| last2= Neto| first2= Mário Popolin| last3= Paulovich| first3= Fernando V.| journal= Electronics| volume= 10| issue= 22| page= 2862}}

* {{ cite web | url=https://fatconference.org/ | title=FAT* Conference on Fairness, Accountability, and Transparency }}

* {{cite web | title='Explainable Artificial Intelligence': Cracking open the black box of AI | website=Computerworld | date=2017-11-02 | url=https://www.computerworld.com.au/article/617359/explainable-artificial-intelligence-cracking-open-black-box-ai/ | ref={{sfnref | Computerworld | 2017}} | access-date=2017-11-02 | archive-date=2020-10-22 | archive-url=https://web.archive.org/web/20201022062307/https://www2.computerworld.com.au/article/617359/explainable-artificial-intelligence-cracking-open-black-box-ai/ | url-status=dead }}

* {{cite web | url=https://www.fatml.org/ | title=FATML Workshop on Fairness, Accountability, and Transparency in Machine Learning }}

* {{cite web | title='Explainable Artificial Intelligence': Cracking open the black box of AI | website=Computerworld | date=2017-11-02 | url=https://www.computerworld.com.au/article/617359/explainable-artificial-intelligence-cracking-open-black-box-ai/ | ref={{sfnref | Computerworld | 2017}} | access-date=2017-11-02 | archive-date=2020-10-22 | archive-url=https://web.archive.org/web/20201022062307/https://www2.computerworld.com.au/article/617359/explainable-artificial-intelligence-cracking-open-black-box-ai/ | url-status=dead }}

* {{cite arXiv | last1=Park | first1=Dong Huk | last2=Hendricks | first2=Lisa Anne | last3=Akata | first3=Zeynep | last4=Schiele | first4=Bernt | last5=Darrell | first5=Trevor | last6=Rohrbach | first6=Marcus | title=Attentive Explanations: Justifying Decisions and Pointing to the Evidence | date=2016-12-14 | eprint=1612.04757 | class=cs.CV }}

* {{cite web | title=Explainable AI: Making machines understandable for humans | website=Explainable AI: Making machines understandable for humans | url=https://explainableai.com/ | ref={{sfnref | Explainable AI: Making machines understandable for humans}} | access-date=2017-11-02}}

* {{cite web | title=Explaining How End-to-End Deep Learning forSteers a Self-Driving CarsCar | website=Parallel Forall | date=20162017-0805-1723 | url=https://devblogs.nvidia.com/parallelforall/explaining-deep-learning-self-driving-carscar/ | ref={{sfnref | Parallel Forall | 20162017}} | access-date=2017-11-02}}

* {{cite web | title=Explaining How End-to-End Deep Learning Steers a Self-Driving Car | website=Parallel Forall | date=2017-05-23 | url=https://devblogs.nvidia.com/parallelforall/explaining-deep-learning-self-driving-car/ | ref={{sfnref | Parallel Forall | 2017}} | access-date=2017-11-02}}

* {{cite web | last=Knight | first=Will | title=DARPA is funding projects that will try to open up AI's black boxes | website=MIT Technology Review | date=2017-03-14 | url=https://www.technologyreview.com/s/603795/the-us-military-wants-its-autonomous-machines-to-explain-themselves/ | access-date=2017-11-02}}

* {{cite arXiv | last1=Alvarez-Melis | first1=David | last2=Jaakkola | first2=Tommi S. |title=A causal framework for explaining the predictions of black-box sequence-to-sequence models | date=2017-07-06 | eprint=1707.01943 | class=cs.LG }}