This new report by the United Nations Office of High Commissioner of Human Rights builds on three prior analyses and new statistical analysis by HRDAG on killings in Syria.
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Assassinations of social leaders in Colombia in 2016–2017
Patrick Ball, César Rodríguez and Valentina Rozo (2018). Asesinatos de líderes sociales en Colombia en 2016–2017: una estimación del universo. Dejusticia and Human Rights Data Analysis Group. August 2018. © 2018 HRDAG. Creative Commons.
Patrick Ball, César Rodríguez and Valentina Rozo (2018). Asesinatos de líderes sociales en Colombia en 2016–2017: una estimación del universo. Dejusticia and Human Rights Data Analysis Group. August 2018. © 2018 HRDAG. Creative Commons.
Update on Work in Guatemala and the AHPN
HRDAG has sampled and analyzed documents at Guatemala's AHPN and has testified against war criminals based on that analysis.
How many people disappeared on 17–19 May 2009 in Sri Lanka?
Patrick Ball and Frances Harrison (2018). How many people disappeared on 17–19 May 2009 in Sri Lanka? Human Rights Data Analysis Group. 12 December 2018.© 2018 HRDAG. Creative Commons.
Patrick Ball and Frances Harrison (2018). How many people disappeared on 17–19 May 2009 in Sri Lanka? Human Rights Data Analysis Group. 12 December 2018.© 2018 HRDAG. Creative Commons.
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.”
Lessons at HRDAG: Making More Syrian Records Usable
If we could glean key missing information from those fields, we would be able to use more records.
Learning a Modular, Auditable and Reproducible Workflow
The modular nature of the workflow and use of Git allowed us to work on different parts of the project from across the country.
Covid-19 Research and Resources
HRDAG is identifying and interpreting the best science we can find to shed light on the global crisis brought on by the novel coronavirus, about which we still know so little. Right now, most of the data on the virus SARS-CoV-2 and Covid-19, the condition caused by the virus, are incomplete and unrepresentative, which means that there is a great deal of uncertainty. But making sense of imperfect datasets is what we do. HRDAG is contributing to a better understanding with explainers, essays, and original research, and we are highlighting trustworthy resources for those who want to dig deeper.
Papers and articles by HRDAG
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Film: Solving for X
Solving for X documents Patrick's team as they travel to Guatemala, Kosovo, and Liberia, helping human rights supporters apply sophisticated computer analysis to human rights events.
HRDAG’s Year in Review: 2020
In 2020, HRDAG provided clarity on issues related to the pandemic, police misconduct, and more.
Estimating the Number of SARS-CoV-2 Infections and the Impact of Mitigation Policies
This Harvard Data Science Review article uses the least unreliable source of pandemic data: reported deaths.
String matching for governorate information in unstructured text
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Lies, Damned Lies and Official Statistics
This essay in the Health and Human Rights Journal addresses attempts to undermine Covid-19 data collection.
HRDAG Welcomes New Staff, Interns and Fellow
HRDAG is delighted to announce five additions to our team: one new staff member, three summer interns, and one fellow.
Quantifying Police Misconduct in Louisiana
HRDAG contributes to the project by helping to classify, filter, extract, and standardize the records so that they can be useful in the database.
Innocence Discovery Lab – Harnessing Large Language Models to Surface Data Buried in Wrongful Conviction Case Documents
Ayyub Ibrahim, Huy Dao, and Tarak Shah (2024). “Innocence Discovery Lab - Harnessing Large Language Models to Surface Data Buried in Wrongful Conviction Case Documents." The Wrongful Conviction Law Review 5 (1):103-25. https://doi.org/10.29173/wclawr112. 31 May, 2024. Copyright (c) 2024 Ayyub Ibrahim, Huy Dao, Tarak Shah. Creative Commons Attribution 4.0 International License.
Ayyub Ibrahim, Huy Dao, and Tarak Shah (2024). “Innocence Discovery Lab – Harnessing Large Language Models to Surface Data Buried in Wrongful Conviction Case Documents.” The Wrongful Conviction Law Review 5 (1):103-25. https://doi.org/10.29173/wclawr112. 31 May, 2024. Copyright (c) 2024 Ayyub Ibrahim, Huy Dao, Tarak Shah. Creative Commons Attribution 4.0 International License.
Lessons at HRDAG: Holding Public Institutions Accountable
Principled Data Processing is a way to prove to someone, usually yourself, that what you did was right.
Reflections: Pivotal Moments in Freetown
The summer of 2002 in Washington, DC, was steamy and hot, which is how I remember my introduction to HRDAG. I had begun working with them, while they were still at AAAS, in the late spring, learning all about their core concepts: duplicate reporting and MSE, controlled vocabularies, inter-rater reliability, data models and more. The days were long, with a second shift more often than not running late into the evening. In addition to all the learning, I also helped with matching for the Chad project – that is, identifying multiple records of the same violation – back when matching was done by hand. But it was not long after I arrived in Washington ...
Where Stats and Rights Thrive Together
Everyone I had the pleasure of interacting with enriched my summer in some way.