Law Commons^™

Outcome Prediction In The Practice Of Law, Mark K. Osbeck, Michael Gilliland

Articles

Business forecasters typically use time-series models to predict future demands, the forecasts informing management decision making and guiding organizational planning. But this type of forecasting is merely a subset of the broader field of predictive analytics, models used by data scientists in all manner of applications, including credit approvals, fraud detection, product-purchase and music-listening recommendations, and even the real-time decisions made by self-driving vehicles. The practice of law requires decisions that must be based on predictions of future legal outcomes, and data scientists are now developing forecasting methods to support the process. In this article, Mark Osbeck and Mike Gilliland …

Bias In, Bias Out, Sandra G. Mayson

All Faculty Scholarship

Police, prosecutors, judges, and other criminal justice actors increasingly use algorithmic risk assessment to estimate the likelihood that a person will commit future crime. As many scholars have noted, these algorithms tend to have disparate racial impacts. In response, critics advocate three strategies of resistance: (1) the exclusion of input factors that correlate closely with race; (2) adjustments to algorithmic design to equalize predictions across racial lines; and (3) rejection of algorithmic methods altogether.

This Article’s central claim is that these strategies are at best superficial and at worst counterproductive because the source of racial inequality in risk assessment lies …

When Whispers Enter The Cloud, Heidi H. Liu

All Faculty Scholarship

With increased awareness of workplace harassment in recent years, the idea of enhanced reporting around sexual misconduct has gained traction. As a result, several technologies – from smartphone apps to well-publicized services – have been introduced with the goals of preventing, enabling reports of, and even predicting sexual misconduct at work, school and in public. But to what extent are these technologies secure and accessible to survivors? This Note documents the existing resources and proposes a framework focusing on privacy and participation for evaluating these tools intended to benefit survivors.