Explainable AI

More than two dozen artificial intelligence experts from business and academia, including Texas McCombs, explored the importance of understanding how machine learning systems arrive at their conclusions so humans can trust those results. This relatively new frontier of explainable AI, or XAI, was scrutinized for two half-day sessions in November during the online Global Analytics Summit held by the McCombs Center for Analytics and Transformative Technologies (CATT). More than 3,500 people registered for the conference, another step in the university's drive to be a thought leader in the field. Experts say that as AI systems become more sophisticated, they become increasingly difficult for humans to interpret. The calculation process turns into what is called a "black box" that even data scientists who create the algorithms can't understand, according to IBM Corp. XAI methods, however, provide transparency so humans can understand the results and assess their accuracy and fairness.