Browsing by Subject "risk assessment"
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Item Algorithmic Risk Assessments in the Hands of Humans(Salem Center, 2020-10-19) Doleac, Jennifer L.; Stevenson, Megan T.Item Hazards Analysis, Building 950, Brooks Air Force Base, Bexar County, Texas(1975) Chin, John L.; Jones, LeslieBased on available information, building 950 is a suitable site for storage of lunar samples. The only apparent hazard is the high shrink-swell capacity of the soil. This soil property should be a primary consideration in any modifications made to the building.Item Regulating Algorithmic Pretrial Risk Assessment: Lessons from Texas Bail Reform Efforts(2020-05) Ashok, ArvindAlgorithmic risk assessment is increasingly used to gauge the “risk” of defendants in a pretrial context. Given the noted socioeconomic disparities and due process issues with the current pretrial system, which frequently relies on monetary bail, algorithmic risk assessments may help judges come to more rational decisions on whether to detain defendants before trial. However, these assessments have also been critiqued for alleged bias, lack of transparency, and a false sense of objectivity. If algorithmic risk assessments continue, careful regulation of their use is likely necessary, but currently lacking. This thesis synthesizes existing literature on pretrial detention, risk assessment, and algorithmic accountability to identify the potential and flaws of algorithmic risk assessment within all the relevant contexts. Then, it provides a case study of the adoption of an algorithmic pretrial risk assessment tool in Texas in order to elucidate how real-world administrators are managing risk assessments and responding to concerns raised in the literature. The finding from both the synthesis and the case study is that better institutional design and active regulation are needed to keep risk assessments democratically accountable, useful to the broader goals of bail reform, and free from damaging politicization. To this end, the final section of this thesis proposes best practices for future administrators of algorithmic risk assessment, taking into account lessons from both Texas and the broader academic literature.