The problems with Clearview AI's facial recognition system, particularly in the hands of police, are myriad and serious. That the technology exists as it does at all raises significant ethical concerns, and how it has been used to feed people into the criminal justice system raises significant due process ones as well. But an article in the New York Times the other day might seem to suggest that it perhaps also has a cuddly side, one that might actually help criminal defendants, instead of just hurting them. But don't be fooled – there is nothing benign about the facial recognition technology pushed by Clearview AI, and even this story ultimately provides no defense for it. It was not the hero here, because the problem it supposedly "solved" was not the problem that actually needed solving.

Researchers at Dartmouth College have found that a computer program widely used by courts to predict an offenders' risk of reoffending is no more fair or accurate than a bunch of random non-experts who were given the same data and asked to make predictions. The program, Correctional Offender Management Profiling for Alternative Sanctions, is used in several states to inform pretrial, parole, and sentencing decisions. And while it may sound sophisticated--COMPAS has 137 variables and a proprietary algorithm--the software performs no better than a simple linear predictor using just two variables. "Claims that secretive and seemingly sophisticated data tools are more accurate and fair than humans are simply not supported by our research findings," said co-author Julia Dressel, an undergraduate who performed the research with Dartmouth computer scientist Hany Farid. For their peer-reviewed study, published Wednesday in Science Advances (Science magazine's open-access "offspring"), Dressel and Farid commissioned human participants through Amazon's Mechanical Turk program.

Machine learning is taking the tech world by storm. Google announced it was open-sourcing Tensor Flow, their machine learning (ML) software, and Microsoft quickly followed suit. Baidu and Amazon unveiled their own deep learning platforms a few months later, while Facebook began supporting the development of two ML frameworks. But the revolution has spread far beyond the tech realm. As ML continues to take over the tech world, companies and researchers outside the tech bubble have started using ML in somewhat strange and surprising ways.

Danaher/Institute for Ethics and Emerging TechnologiesAlgorithms are everywhere, and in most ways they make our lives better. In the simplest terms, algorithms are procedures or formulas aimed at solving problems. Implemented on computers, they sift through big databases to reveal compatible lovers, products that please, faster commutes, news of interest, stocks to buy, and answers to queries. Dud dates or boring book recommendations are no big deal. But John Danaher, a lecturer in the law school at the National University of Ireland, warns that algorithmic decision-making takes on a very different character when it guides government monitoring and enforcement efforts.