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Data Mining for Good: CJA Drink + Think
HRDAG Report on Disappeared Tamils in Army Custody in Sri Lanka
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.
Palantir Has Secretly Been Using New Orleans to Test Its Predictive Policing Technology
One of the researchers, a Michigan State PhD candidate named William Isaac, had not previously heard of New Orleans’ partnership with Palantir, but he recognized the data-mapping model at the heart of the program. “I think the data they’re using, there are serious questions about its predictive power. We’ve seen very little about its ability to forecast violent crime,” Isaac said.
Quantifying Injustice
“In 2016, two researchers, the statistician Kristian Lum and the political scientist William Isaac, set out to measure the bias in predictive policing algorithms. They chose as their example a program called PredPol. … Lum and Isaac faced a conundrum: if official data on crimes is biased, how can you test a crime prediction model? To solve this technique, they turned to a technique used in statistics and machine learning called the synthetic population.”
Controlled vocabulary
Welcome!
Press Release, Timor-Leste, February 2006
Learning Day by Day: Quantitative Research at the AHPN
HRDAG Wins the Rafto Prize
How Review of Police Data Verified Neglect of Missing Black Women
Learning a Modular, Auditable and Reproducible Workflow
Donate with Cryptocurrency
HRDAG Adds Three New Board Members
The Allegheny Family Screening Tool’s Overestimation of Utility and Risk
Anjana Samant, Noam Shemtov, Kath Xu, Sophie Beiers, Marissa Gerchick, Ana Gutierrez, Aaron Horowitz, Tobi Jegede, Tarak Shah (2023). The Allegheny Family Screening Tool’s Overestimation of Utility and Risk. Logic(s). 13 December, 2023. Issue 20.
Updated Casualty Count for Syria
Reflections: The People Who Make the Data
¿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.
Matching the Libro Amarillo to Historical Human Rights Datasets in El Salvador
Patrick Ball (2014). A memo accompanying the release of The Yellow Book. August 20, 2014. © 2014 HRDAG. Creative Commons BY-NC-SA.[pdf español]
Cuentas y mediciones de la criminalidad y de la violencia
Exploración y análisis de los datas para comprender la realidad. Patrick Ball y Michael Reed Hurtado. 2015. Forensis 16, no. 1 (July): 529-545. © 2015 Instituto Nacional de Medicina Legal y Ciencias Forenses (República de Colombia).