methodology-5
Explainable artificial intelligence
3260 papers • 126 benchmarks • 313 datasets
XAI refers to methods and techniques in the application of artificial intelligence (AI) such that the results of the solution can be understood by humans. It contrasts with the concept of the "black box" in machine learning where even its designers cannot explain why an AI arrived at a specific decision. XAI may be an implementation of the social right to explanation. XAI is relevant even if there is no legal right or regulatory requirement—for example, XAI can improve the user experience of a product or service by helping end users trust that the AI is making good decisions. This way the aim of XAI is to explain what has been done, what is done right now, what will be done next and unveil the information the actions are based on. These characteristics make it possible (i) to confirm existing knowledge (ii) to challenge existing knowledge and (iii) to generate new assumptions.
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Axiomatic Attribution for Deep Networks
Ankur Taly, Mukund Sundararajan, Qiqi Yan•Fri Mar 03 2017
The problem of attributing the prediction of a deep network to its input features, a problem previously studied by several other works, is studied and two fundamental axioms— Sensitivity and Implementation Invariance that attribution methods ought to satisfy are identified.
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