Explainable artificial intelligence: Difference between revisions

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== Recent developments ==

Layerwise Relevance Propagation (LRP), first described in 2015, is a technique for determining which features in a particular input vector contribute most strongly to a neural network’s output.<ref name="Shiebler 2017">{{cite web | last=Shiebler | first=Dan | title=Understanding Neural Networks with Layerwise Relevance Propagation and Deep Taylor Series | website=Dan Shiebler | date=2017-04-16 | url=http://dshieble.github.io/2017-04-16-deep-taylor-lrp/ | access-date=2017-11-03}}</ref><ref name="Bach Binder Montavon Klauschen p=e0130140">{{cite journal | last=Bach | first=Sebastian | last2=Binder | first2=Alexander | last3=Montavon | first3=Grégoire | last4=Klauschen | first4=Frederick | last5=Müller | first5=Klaus-Robert | last6=Samek | first6=Wojciech | editor-last=Suarez | editor-first=Oscar Deniz | title=On Pixel-Wise Explanations for Non-Linear Classifier Decisions by Layer-Wise Relevance Propagation | journal=PLOS ONE | publisher=Public Library of Science (PLoS) | volume=10 | issue=7 | date=2015-07-10 | issn=1932-6203 | doi=10.1371/journal.pone.0130140 | page=e0130140}}</ref>

As regulators, official bodies and general users dependency on AI-based dynamic systems, clearer accountability will be required for decision making processes to ensure trust and transparency. Evidence of this requirement gaining more momentum can be seen with the launch of the first global conference exclusively dedicated to this emerging discipline, the 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://home.earthlink.net/~dwaha/research/meetings/ijcai17-xai/|website=Earthlink|publisher=IJCAI |accessdate=17 July 2017}}</ref>