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Celebrating Women in Statistics
In her work on statistical issues in criminal justice, Lum has studied uses of predictive policing—machine learning models to predict who will commit future crime or where it will occur. In her work, she has demonstrated that if the training data encodes historical patterns of racially disparate enforcement, predictions from software trained with this data will reinforce and—in some cases—amplify this bias. She also currently works on statistical issues related to criminal “risk assessment” models used to inform judicial decision-making. As part of this thread, she has developed statistical methods for removing sensitive information from training data, guaranteeing “fair” predictions with respect to sensitive variables such as race and gender. Lum is active in the fairness, accountability, and transparency (FAT) community and serves on the steering committee of FAT, a conference that brings together researchers and practitioners interested in fairness, accountability, and transparency in socio-technical systems.
Working Where Statistics and Human Rights Meet
Robin Mejia and Megan Price (2018). Working Where Statistics and Human Rights Meet. Chance (special issue). February 2018. © 2018 CHANCE.
The Statistics of Genocide
Patrick Ball and Megan Price (2018). The Statistics of Genocide. Chance (special issue). February 2018. © 2018 CHANCE.
On or off the record? Detecting patterns of silence about death in Guatemala’s National Police Archive
Tamy Guberek and Margaret Hedstrom (2017). On or off the record? Detecting patterns of silence about death in Guatemala’s National Police Archive. Archival Science. 9 February 2017. © Springer. DOI 10.1007/s10502-017-9274-3.
Our Thoughts on #metoo
Momentous Verdict against Hissène Habré
Locating Hidden Graves in Mexico
Human Rights Violations: How Do We Begin Counting the Dead?
Evaluation of the Kosovo Memory Book at Pristina
The Limits of Observation for Understanding Mass Violence.
Publications
Haiti
Truth Commissioner
From the Guatemalan military to the South African apartheid police, code cruncher Patrick Ball singles out the perpetrators of political violence.
Welcoming our new Technical Lead
Hunting for Mexico’s mass graves with machine learning
“The model uses obvious predictor variables, Ball says, such as whether or not a drug lab has been busted in that county, or if the county borders the United States, or the ocean, but also includes less-obvious predictor variables such as the percentage of the county that is mountainous, the presence of highways, and the academic results of primary and secondary school students in the county.”
Who Did What to Whom? Planning and Implementing a Large Scale Human Rights Data Project
Patrick Ball. Who Did What to Whom? Planning and Implementing a Large Scale Human Rights Data Project. © 1996 American Association for the Advancement of Science.
¿Quién le hizo qué a quién? Planear e implementar un proyecto a gran escala de información en derechos humanos.
Patrick Ball (2008). “¿Quién le hizo qué a quién? Planear e implementar un proyecto a gran escala de información en derechos humanos.” (originally in English at AAAS) Translated by Beatriz Verjerano. Palo Alto, California: Benetech.