Everyone is talking about artificial intelligence these days. As AI matures, it will have higher impacts on the industries trusting it. It will help organizations enhance customer experiences, improve logistics, automate workloads, predict performance, increase efficiency and output, manage and analyze data, and predict consumer behavior. Whether it’s for compliance reasons or to eliminate bias, there is a need for humans to understand the decision-making algorithm of the underlying AI system. This is where explainable AI comes into the picture.
In an article for The Enterprisers Project, Ness CTO, Moshe Kranc when asked about potential use cases for explainable AI, provided an answer that is both simple and far-reaching: “Any use case that impacts people’s lives and could be tainted by bias.” He shares a few examples of decisions likely to be increasingly made by machines, but that will fundamentally require trust, auditability, and other characteristics of explainable AI.
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