Epstein Secrets

ited in the DOJ Report. See DOJ Report at 38-39 nn.20-24. " See DOJ Report at 28 (discussing multiple algorithmic measures of racial bias). 15 See Melissa Hamilton, Debating Algorithmic Fairness, 52 UC DAVIS L REV. ONLINE 261 (2019); Jay P. Singh, Predictive Validi?Perfonnance Indicators in Violent Risk Assessm

rs Legislative Committee 52 Duane Street, 10.K Floor New York, NY 1007 Tel: (212) 417-8738 multidisciplinary team that includes policymakers and stakeholdersi3—rather than solely data scientists. For example, the decisions on the cut-points, which necessarily impact fairness measures such as false positive

ited in the DOJ Report. See DOJ Report at 38-39 nn.20-24. " See DOJ Report at 28 (discussing multiple algorithmic measures of racial bias). 15 See Melissa Hamilton, Debating Algorithmic Fairness, 52 UC DAVIS L REV. ONLINE 261 (2019); Jay P. Singh, Predictive Validi?Perfonnance Indicators in Violent Risk Assessm

rs Legislative Committee 52 Duane Street, 10.K Floor New York, NY 1007 Tel: (212) 417-8738 multidisciplinary team that includes policymakers and stakeholdersi3—rather than solely data scientists. For example, the decisions on the cut-points, which necessarily impact fairness measures such as false positive

ited in the DOJ Report. See DOJ Report at 38-39 nn.20-24. " See DOJ Report at 28 (discussing multiple algorithmic measures of racial bias). 15 See Melissa Hamilton, Debating Algorithmic Fairness, 52 UC DAVIS L REV. ONLINE 261 (2019); Jay P. Singh, Predictive Validi?Perfonnance Indicators in Violent Risk Assessm

rs Legislative Committee 52 Duane Street, 10.K Floor New York, NY 1007 Tel: (212) 417-8738 multidisciplinary team that includes policymakers and stakeholdersi3—rather than solely data scientists. For example, the decisions on the cut-points, which necessarily impact fairness measures such as false positive