Carnegie Mellon Transparency Reports Make AI Decision-Making Accountable

A team of CMU researchers led by Associate Professor Anupam Datta have developed new measurement methods that provide important insight into how machine-learning algorithms make decisions about things like credit applications, job opportunities and medical diagnoses. 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?