Explainable artificial intelligence: Difference between revisions

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