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First Things First: Assessing Data Quality Before Model Quality.
Anita Gohdes and Megan Price (2013). Journal of Conflict Resolution, Volume 57 Issue 6 December 2013. © 2013 Journal of Conflict Resolution. All rights reserved. Reprinted with permission of SAGE. [online abstract]DOI: 10.1177/0022002712459708.
Truth Commissioner
From the Guatemalan military to the South African apartheid police, code cruncher Patrick Ball singles out the perpetrators of political violence.
How Structuring Data Unburies Critical Louisiana Police Misconduct Data
Syria’s status, the migrant crisis and talking to ISIS
In this week’s “Top Picks,” IRIN interviews HRDAG executive director Patrick Ball about giant data sets and whether we can trust them. “No matter how big it is, data on violence is always partial,” he says.
How Data Scraping Provides Insights into Immigrant Arrests
Documents of war: Understanding the Syrian Conflict
Megan Price, Anita Gohdes, and Patrick Ball. 2015. Significance 12, no. 2 (April): 14–19. doi: 10.1111/j.1740-9713.2015.00811.x. © 2015 The Royal Statistical Society. All rights reserved. [online abstract]
Using Math and Science to Count Killings in Syria
To predict and serve?
Kristian Lum and William Isaac (2016). To predict and serve? Significance. October 10, 2016. © 2016 The Royal Statistical Society.
Setting the Record Straight on Predictive Policing and Race
William Isaac and Kristian Lum (2018). Setting the Record Straight on Predictive Policing and Race. In Justice Today. 3 January 2018. © 2018 In Justice Today / Medium.
Talks
verdata: An R package for analyzing data from the Truth Commission in Colombia
Maria Gargiulo, María Julia Durán, Paula Andrea Amado, and Patrick Ball (2024). verdata: An R package for analyzing data from the Truth Commission in Colombia. The Journal of Open Source Software. 6 January, 2024. 9(93), 5844, https://doi.org/10.21105/joss.05844. Creative Commons Attribution 4.0 International License.
Trips to and from Guatemala
Here’s how an AI tool may flag parents with disabilities
HRDAG contributed to work by the ACLU showing that a predictive tool used to guide responses to alleged child neglect may forever flag parents with disabilities. “These predictors have the effect of casting permanent suspicion and offer no means of recourse for families marked by these indicators,” according to the analysis from researchers at the ACLU and the nonprofit Human Rights Data Analysis Group. “They are forever seen as riskier to their children.”
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.”
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.
At Toronto’s Tamil Fest, human rights group seeks data on Sri Lanka’s civil war casualties
Earlier this year, the Canadian Tamil Congress connected with HRDAG to bring its campaign to Toronto’s annual Tamil Fest, one of the largest gatherings of Canada’s Sri Lankan diaspora.
Ravichandradeva, along with a few other volunteers, spent the weekend speaking with festival-goers in Scarborough about the project and encouraging them to come forward with information about deceased or missing loved ones and friends.
“The idea is to collect thorough, scientifically rigorous numbers on the total casualties in the war and present them as a non-partisan, independent organization,” said Michelle Dukich, a data consultant with HRDAG.