698 results for search: %EA%B4%91%EA%B3%A0%EB%AC%B8%EC%9D%98%E2%96%B7%E0%B4%A0%E2%9D%B6%E0%B4%A0%E3%85%A1%E2%9D%BD%E2%9D%BD%E2%9D%BC%E2%9D%BB%E3%85%A1%E2%9D%BD%E2%9D%BC%E2%9D%BC%E2%9D%BD%E2%96%B7%EA%B8%B0%EA%B3%84%EB%A9%B4%EA%B0%90%EC%84%B1%EB%A7%88%EC%82%AC%EC%A7%80%E3%81%8D%EA%B4%91%EA%B3%A0%E2%94%AE%EB%AC%B8%EC%9D%98%E2%86%82%EA%B8%B0%EA%B3%84%EB%A9%B4%E7%9C%98%EA%B0%90%EC%84%B1%EB%A7%88%EC%82%AC%EC%A7%80%E5%A6%B8emendatory/feed/rss2/press-release-chad-2010jan
Update of Iraq and Syria Data in New Paper
In Syria, Uncovering the Truth Behind a Number
How many social movement leaders have been killed in Colombia? An estimate and analysis
The Devil is in the Details: Interrogating Values Embedded in the Allegheny Family Screening Tool
Anjana Samant, Noam Shemtov, Kath Xu, Sophie Beiers, Marissa Gerchick, Ana Gutierrez, Aaron Horowitz, Tobi Jegede, Tarak Shah (2023). The Devil is in the Details: Interrogating Values Embedded in the Allegheny Family Screening Tool. ACLU. Summer 2023.
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.”
The True Dangers of AI are Closer Than We Think
William Isaac is quoted.
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.
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.
Perú
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.
Data Mining for Good: CJA Drink + Think
How Data Processing Uncovers Misconduct in Use of Force in Puerto Rico
Liberian Truth and Reconciliation Commission Data
Reflections: A Simple Plan
HRDAG Retreat 2022
Rise of the racist robots – how AI is learning all our worst impulses
“If you’re not careful, you risk automating the exact same biases these programs are supposed to eliminate,” says Kristian Lum, the lead statistician at the San Francisco-based, non-profit Human Rights Data Analysis Group (HRDAG). Last year, Lum and a co-author showed that PredPol, a program for police departments that predicts hotspots where future crime might occur, could potentially get stuck in a feedback loop of over-policing majority black and brown neighbourhoods. The program was “learning” from previous crime reports. For Samuel Sinyangwe, a justice activist and policy researcher, this kind of approach is “especially nefarious” because police can say: “We’re not being biased, we’re just doing what the math tells us.” And the public perception might be that the algorithms are impartial.
Guatemala CIIDH Data
Syria’s celebrations muted by evidence of torture in Assad’s notorious prisons
The Human Rights Data Analysis Group, an independent scientific human rights organization based in San Francisco, has counted at least 17,723 people killed in Syrian custody from 2011 to 2015 — around 300 every week — almost certainly a vast undercount, it says.