658 results for search: %E5%A4%A7%E5%85%AC%E5%8F%B8%E7%9A%84%E4%BC%98%E5%8A%BF%E5%92%8C%E5%8A%A3%E5%8A%BF-%E3%80%90%E2%9C%94%EF%B8%8F%E6%8E%A8%E8%8D%90KK37%C2%B7CC%E2%9C%94%EF%B8%8F%E3%80%91-%E5%90%8C%E6%B2%BB%E6%AF%94%E5%85%89%E7%BB%AA%E5%A4%A7%E5%87%A0%E5%B2%81-%E5%A4%A7%E5%85%AC%E5%8F%B8%E7%9A%84%E4%BC%98%E5%8A%BF%E5%92%8C%E5%8A%A3%E5%8A%BFzo9xn-%E3%80%90%E2%9C%94%EF%B8%8F%E6%8E%A8%E8%8D%90KK37%C2%B7CC%E2%9C%94%EF%B8%8F%E3%80%91-%E5%90%8C%E6%B2%BB%E6%AF%94%E5%85%89%E7%BB%AA%E5%A4%A7%E5%87%A0%E5%B2%81p2dn-%E5%A4%A7%E5%85%AC%E5%8F%B8%E7%9A%84%E4%BC%98%E5%8A%BF%E5%92%8C%E5%8A%A3%E5%8A%BFblgpy-%E5%90%8C%E6%B2%BB%E6%AF%94%E5%85%89%E7%BB%AA%E5%A4%A7%E5%87%A0%E5%B2%81r26q/feed/rss2/tchad-faqs-fr
Courts and police departments are turning to AI to reduce bias, but some argue it’ll make the problem worse
Kristian Lum: “The historical over-policing of minority communities has led to a disproportionate number of crimes being recorded by the police in those locations. Historical over-policing is then passed through the algorithm to justify the over-policing of those communities.”
Estimating the Number of SARS-CoV-2 Infections and the Impact of Mitigation Policies in the United States
James Johndrow, Patrick Ball, Maria Gargiulo, and Kristian Lum. (2020). Estimating the Number of SARS-CoV-2 Infections and the Impact of Mitigation Policies in the United States. Harvard Data Science Review. 24 November, 2020. © The Authors, 2020, CC BY 4.0. https://doi.org/10.1162/99608f92.7679a1ed
A Comparison of Marginal and Conditional Models for Capture–Recapture Data with Application to Human Rights Violations Data
Shira Mitchell, Al Ozonoff, Alan Zaslavsky, Bethany Hedt-Gauthier, Kristian Lum and Brent Coull (2013). A Comparison of Marginal and Conditional Models for Capture-Recapture Data with Application to Human Rights Violations Data. Biometrics, Volume 69, Issue 4, pages 1022–1032, December 2013. © 2013, The International Biometric Society. DOI: 10.1111/biom.12089.
BJS Report on Arrest-Related Deaths: True Number Likely Much Greater
Can the Armed Conflict Become Part of Colombia’s History?
RustConf 2019, and systems programming as a data scientist
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.
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.
Funding
Tech Note: Chicago Missing Persons
Civil War in Syria: The Internet as a Weapon of War
Suddeutsche Zeitung writer Hakan Tanriverdi interviews HRDAG affiliate Anita Gohdes and writes about her work on the Syrian casualty enumeration project for the UN Office of the High Commissioner for Human Rights. This article, “Bürgerkrieg in Syrien: Das Internet als Kriegswaffe,” is in German.
Using Data and Statistics to Bring Down Dictators
In this story, Guerrini discusses the impact of HRDAG’s work in Guatemala, especially the trials of General José Efraín Ríos Montt and Colonel Héctor Bol de la Cruz, as well as work in El Salvador, Syria, Kosovo, and Timor-Leste. Multiple systems estimation and the perils of using raw data to draw conclusions are also addressed.
Megan Price and Patrick Ball are quoted, especially in regard to how to use raw data.
“From our perspective,” Price says, “the solution to that is both to stay very close to the data, to be very conservative in your interpretation of it and to be very clear about where the data came from, how it was collected, what its limitations might be, and to a certain extent to be skeptical about it, to ask yourself questions like, ‘What is missing from this data?’ and ‘How might that missing information change these conclusions that I’m trying to draw?’”
HRDAG’s Year in Review: 2022
Predictive policing violates more than it protects
William Isaac and Kristian Lum. Predictive policing violates more than it protects. USA Today. December 2, 2016. © USA Today.
Ten Years and Counting in Guatemala
Celebrating Ten Years of Data from the AHPN
Analizando los patrones de violencia en Colombia con más de 100 bases de datos
Ciencia de datos para trazar un mapa de la crueldad a la mexicana
From the article: Esta entidad, que existe desde 1991, es liderada por su fundador, Patrick Ball, un científico que acumula una experiencia de más de 25 años realizando análisis cuantitativos en los lugares y en las situaciones más convulsos del planeta. Sobre su colaboración con el proyecto del predictor de fosas clandestinas en México, único en el mundo, Ball afirmó en entrevista:
“Cuando hablamos de crímenes de lesa humanidad estamos hablando de instituciones, de organizaciones grandes, cometiendo miles o centenares de miles de violaciones a víctimas distribuidas sobre una geografía enorme. Para entender los patrones en esas violaciones, la estadística puede brindar una mirada sobre quiénes son los responsables materiales e intelectuales, quiénes son las víctimas y dónde o cuándo pasaron esas violaciones. Pero la estadística no es contabilidad, pues no estamos hablando solamente de las violaciones que podemos ver, sino que también debemos calcular las violaciones no observadas, las escondidas, invisibles, para incluir en nuestro análisis la totalidad de las violaciones”.