27 May '16, 3pm

Carnegie Mellon Transparency Reports Make AI Decision-Making Accountable

Machine-learning algorithms increasingly make decisions about credit, medical diagnoses, personalized recommendations, advertising and job opportunities, among other things, but exactly how usually remains a mystery. Now, new measurement methods developed by Carnegie Mellon University researchers could provide important insights to this process. Was it a person's age, gender or education level that had the most influence on a decision? Was it a particular combination of factors? CMU's Quantitative Input Influence (QII) measures can provide the relative weight of each factor in the final decision, said Anupam Datta , associate professor of computer science and electrical and computer engineering . "Demands for algorithmic transparency are increasing as the use of algorithmic decision-making systems grows and as people realize the potential of these systems to introduce or p...

Full article: http://www.ecnmag.com/news/2016/05/carnegie-mellon-transp...

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