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RustConf 2019, and systems programming as a data scientist

It could make sense to use Rust as a data journalist for in-browser computations, and other thoughts from RustConf.

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.


Los asesinatos de líderes sociales que quedan fuera de las cuentas

Una investigación de Dejusticia y Human Rights Data Analysis Group concluyó que hay un subconteo en los asesinatos de líderes sociales en Colombia. Es decir, que el aumento de estos crímenes en 2016 y 2017 podría ser incluso mayor al reportado por las organizaciones y por las cifras oficiales.


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”.


Tech Note

Using large language models for structured information extraction from the Innocence Project New Orleans' wrongful conviction case files. Exoneration documents, secured during legal proceedings that aim to right the wrongs of justice, are invaluable for understanding wrongful convictions. They cast a spotlight on law enforcement actions, revealing systemic challenges. Yet, finding and leveraging usable information within these collections remains a formidable task for researchers and advocates, due to their volume and unstructured heterogeneity. This post introduces the methodology of the Innocence Discovery Lab, a collaboration between Innoce...

IRR: Agreement Among Coders is Key

For years I have been engaged in a quantitative study at Guatemala’s Historic Archive of the National Police, or AHPN. (See the blogposts below.) In this study coders collect data on sheets of paper according to criteria established and explained in manuals. But when collecting data, there’s always room for human error—this is why the validity of the study hinges on verifying that coders use the correct criteria. It is important to mention that the mainstay of coding is the use of a controlled vocabulary. A controlled vocabulary gives analysts a framework, or frame of reference, when converting qualitative information into categories ...

Welcome!

As of today, the Human Rights Data Analysis Group (HRDAG) is an independent* non-profit! It's been a long time coming, and we're delighted to have gotten to this point. HRDAG is a non-profit, non-partisan organization that applies rigorous science to the analysis of human rights violations around the world; for more information, see our About Us page. Benetech has spun out the scientific and statistical part of the Human Rights Program to HRDAG. The spinout includes (as staff) me -- Patrick Ball -- and Dr Megan Price, as well as our many part-time scientific and field consultants (a list is here). The software and technology component of our work -- ...

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.

The Limits of Observation for Understanding Mass Violence.

Megan Price and Patrick Ball. 2015. Canadian Journal of Law and Society / Revue Canadienne Droit et Société volume 30 issue 2 (June): 1-21. doi:10.1017/cls.2015.24. © Cambridge University Press. All rights reserved. Restricted access.

Welcoming Our 2019 Data Science Fellow

We’re pleased to announce that Camille Fassett has joined our team as our new data science fellow.

The Great Lessons in Research at the Archive

Doing an investigation on the contents of the Archive brought with it three major lessons. The first big lesson was the constant movement (nothing was static), The second great lesson was that everything evolved (the changes were a constant). The third major lesson was to discover how two institutions can work together while geographically far apart. The constant movement As there were other processes being carried out at the Archive, everything was in constant movement. In other words, one day the documents were in X location and tomorrow they may be in location Y or dispersed in multiple locations. This made it impossible to know with certai...

How Predictive Policing Reinforces Bias

Algorithmic tools like PredPol were supposed to reduce bias. But HRDAG has found that racial bias is baked into the data used to train the tools.

DATNAV: New Guide to Navigate and Integrate Digital Data in Human Rights Research

DatNav is the result of a collaboration between Amnesty International, Benetech, and The Engine Room, which began in late 2015 culminating in an intense four-day writing sprint facilitated by Chris Michael and Collaborations for Change in May 2016. HRDAG consultant Jule Krüger is a contributor, and HRDAG director of research Patrick Ball is a reviewer.

DatNav is the result of a collaboration between Amnesty International, Benetech, and The Engine Room, which began in late 2015 culminating in an intense four-day writing sprint facilitated by Chris Michael and Collaborations for Change in May 2016. HRDAG consultant Jule Krüger is a contributor, and HRDAG director of research Patrick Ball is a reviewer.


Low-risk population size estimates in the presence of capture heterogeneity

James Johndrow, Kristian Lum and Daniel Manrique-Vallier (2019). Low-risk population size estimates in the presence of capture heterogeneity. Biometrika, asy065, 22 January 2019. © 2019 Biometrika Trust. https://doi.org/10.1093/biomet/asy065

James Johndrow, Kristian Lum and Daniel Manrique-Vallier (2019). Low-risk population size estimates in the presence of capture heterogeneityBiometrika, asy065, 22 January 2019. © 2019 Biometrika Trust. https://doi.org/10.1093/biomet/asy065


Counting Civilian Casualties: An Introduction to Recording and Estimating Nonmilitary Deaths in Conflict

ed. by Taylor B. Seybolt, Jay D. Aronson, and Baruch Fischhoff. Oxford University Press. © 2013 Oxford University Press. All rights reserved.

The following four chapters are included:

— Todd Landman and Anita Gohdes (2013). “A Matter of Convenience: Challenges of Non-Random Data in Analyzing Human Rights Violations in Peru and Sierra Leone.”

— Jeff Klingner and Romesh Silva (2013). “Combining Found Data and Surveys to Measure Conflict Mortality.”

— Daniel Manrique-Vallier, Megan E. Price, and Anita Gohdes (2013). “Multiple-Systems Estimation Techniques for Estimating Casualties in Armed Conflict.”

— Jule Krüger, Patrick Ball, Megan Price, and Amelia Hoover Green (2013). “It Doesn’t Add Up: Methodological and Policy Implications of Conflicting Casualty Data.”


La importancia de la estadística

Patrick Ball (2018). La importancia de la estadística. Ibero. La revista de la universidad Iberoamericana. August-September 2018. © 2018 Universidad Iberoamericana Ciudad de México. Pp. 50-51.

Patrick Ball (2018). La importancia de la estadística. Ibero. La revista de la universidad Iberoamericana. August-September 2018. © 2018 Universidad Iberoamericana Ciudad de México. Pp. 50-51.


Police transparency expands with new national database — except Michigan

Tarak Shah is quoted with regard to the National Police Index: “Police often avoid accountability by moving to another agency rather than face discipline. This tool, allowing anyone to look up and track the histories of such officers, provides an invaluable service for the human rights community in our fight against impunity.”


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.

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.


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.

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.


The World According to Artificial Intelligence (Part 1)

The World According to Artificial Intelligence: Targeted by Algorithm (Part 1)

The Big Picture: The World According to AI explores how artificial intelligence is being used today, and what it means to those on its receiving end.

Patrick Ball is interviewed: “Machine learning is pretty good at finding elements out of a huge pool of non-elements… But we’ll get a lot of false positives along the way.”


Our work has been used by truth commissions, international criminal tribunals, and non-governmental human rights organizations. We have worked with partners on projects on five continents.

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