Companies want explainable AI, vendors respond

Fed up with the bribery, insider trading, embezzlement and money laundering committed by white-collar criminals? What if there was an app that could help nab these crooks by using the same machine learning tools and geospatial data increasingly relied upon by police to predict where the next burglary, drug deal or assault might go down? Sam Lavigne, co-creator of the White Collar Crime Risk Zones app, was onstage at the recent Strata Data Conference in New York, claiming to be able to do just that. "We used instances of financial malfeasance; density of nonprofit organizations, liquor stores, bars and clubs; and density of investment advisers," a straight-faced Lavigne said to an audience of data experts who immediately got the dark humor. For although the White Collar Crime Risk Zones app was indeed built -- using historical data from the Financial Industry Regulatory Authority -- its purpose is not to track white-collar crime, but to draw attention to the danger these kinds of applications, and the data they rely upon, present.