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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
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
FAQs on Predictive Policing and Bias
Last month Significance magazine published an article on the topic of predictive policing and police bias, which I co-authored with William Isaac. Since then, we've published a blogpost about it and fielded a few recurring questions. Here they are, along with our responses.
Do your findings still apply given that PredPol uses crime reports rather than arrests as training data?
Because this article was meant for an audience that is not necessarily well-versed in criminal justice data and we were under a strict word limit, we simplified language in describing the data. The data we used is a version of the Oakland Police Department’s crime report...
Rapid response to: Civilian deaths from weapons used in the Syrian conflict
On November 4, 2015, the BMJ published our "Rapid Response" to Civilian deaths from weapons used in the Syrian conflict (BMJ 2015;351:h4736). The response was co-authored by Megan Price, Anita Gohdes, Jay Aronson (Carnegie Mellon University, Center for Human Rights Science), and Christopher McNaboe (Carter Center, Syria Conflict Mapping Project).
We have three concerns about this article. First, the article apportions responsibility for casualties to particular perpetrator organizations based on a single snapshot of territorial control that ignores the numerous (and well-documented) changes in this phenomenon over time. Second, combining Syrian ...
Testimonials
HRDAG is honored to work with a diverse set of partners. These organizations and the individuals that operate them are critical to our success, and our goal is to be critical to theirs. Here are a few quotes from our colleagues.
"Over the last two years, Dr Patrick Ball has spoken several times to relevant AI staff on the use (and mis-use) of quantitative data in human rights work. Each time, people rave about it afterwards commenting on Patrick's inimical skills to convey the complexity of statistical science in an accessible, relevant and fun way. This year, we also organised small meetings with individual teams who have to crunch 'big data' ...
HRDAG is hiring – technical lead
If this could be you, let us know. Also, please feel free to pass on this link to great people.
Job Title. Technical lead with a hacker's heart
Location. A cool office in SOMA, San Francisco. You need to be on-site with us.
What we do. The Human Rights Data Analysis Group (HRDAG) develops statistical techniques to measure human rights atrocities. Our work helps bring dictators to justice through data analysis of human rights atrocities around the world. Over more than 20 years, our small team has developed technology and statistical techniques to take disjoint, incomplete, and inaccurate information from conflict zones and process it to identify ...
Multiple Systems Estimation: Stratification and Estimation
<< Previous post, MSE: The Matching Process
Q10. What is stratification?
Q11. [In depth] How do HRDAG analysts approach stratification, and why is it important?
Q12. How does MSE find the total number of violations?
Q13. [In depth] What are the assumptions of two-system MSE (capture-recapture)? Why are they not necessary with three or more systems?
Q14. What statistical model(s) does HRDAG typically use to calculate MSE estimates? (more…)
12 Questions about Using Data Analysis to Bring Guatemalan War Criminals to Justice
When people talk about war criminals in Guatemala, which war are they talking about?
They’re talking about the Guatemalan civil war, which began in 1960 and ended in 1996. That’s thirty-six years of civil war. Even though it ended almost two decades ago, Guatemala is still recovering from it. At its simplest, this civil war story was right-wing government forces fighting leftist rebels. But it went deeper than that, of course. The majority of the rebel forces was composed of indigenous peoples, primarily the Maya, (more…)
Multiple Systems Estimation: Does it Really Work?
<< Previous post, MSE: Stratification and Estimation
Q15. Are there other MSE models one might use with human rights data?
Q16. Is it possible to use MSE to model non-lethal human rights violations?
Q17. I am concerned about using MSE with my data, because the datasets were gathered by opposing organizations. Victims who were reported to an NGO were very unlikely to be reported to state sources, but also very likely to be reported to religious organizations. Won't that cause the overlaps between the NGO list and the state list to be artificially low, and the overlaps between the NGO list and the church list to be artificially high? Does ...
Mexico
HRDAG and our partners Data Cívica and the Iberoamericana University created a machine-learning model to predict which counties (municipios) in Mexico have the highest probability of unreported hidden graves. The predictions help advocates to bring public attention and government resources to search for the disappeared in the places where they are most likely to be found.
Context
For more than ten years, Mexican authorities have been discovering hidden graves (fosas clandestinas). The casualties are attributed broadly—and sometimes inaccurately—to the country’s “drug war,” but the motivations and perpetrators behind the mass murders ...
India
In 2009, as Indians debated institutional reform of their security forces in the wake of the previous year's Mumbai attacks, HRDAG issued a groundbreaking report about the human cost of suspending the rule of law during a violent counterinsurgency campaign in the Indian state of Punjab. Together with our partner Ensaaf, HRDAG released findings that cast substantial doubt on the Indian government's past explanations and justifications for disappearances and extrajudicial killings during the height of the Punjab counterinsurgency in the early 1990s. These findings contribute to an increasing body of knowledge that informs policy questions about the ...
Training with HRDAG: Rules for Organizing Data and More
I had the pleasure of working with Patrick Ball at the HRDAG office in San Francisco for a week during summer 2016. I knew Patrick from two workshops he previously hosted at the University of Washington’s Centre for Human Rights (UWCHR). The workshops were indispensable to us at UWCHR as we worked to publish a number of datasets on human rights violations during the El Salvador Civil War. The training was all the more helpful because the HRDAG team was so familiar with the data. As part of an impressive career which took him from Ethiopia and Kosovo to Haiti and El Salvador among others, Patrick himself had worked on gathering and analysing ...
FAQ about the JEP-CEV-HRDAG data integration and statistical estimation project
1. Is there a single source of information about the victims of the armed conflict in Colombia?
No. Colombia has an extensive documentation process for victims of the armed conflict. Hundreds of institutions, victims' organizations, and civil society organizations have focused their efforts on recording this information. However, each entity or organization develops their documentation process with its own limitations related to technical, logistical, social, and missionary capacities. No entity or organization is able to document the complete universe of victims. This is because it is impossible for them to reach every part of the country, ...
Welcoming Our 2018 Data Science Fellow
Shemika Lamare has joined the HRDAG team as our new data science fellow.
How to Become a Data Scientist: My Lessons at HRDAG
I will use the skills and culture I learned from HRDAG’s team to understand how the conflict has affected the people in my country.
Colombia Report
Benetech Human Rights Program and Corporación Punto de Vista Issues Report on
Sexual Violence in Colombia
Researchers Find that Data About Sexual Violence is Difficult To Collect and Subject to Misinterpretation
May 2, 2011, Palo Alto, CA — The Benetech Human Rights Program has issued a report with the Colombian NGO Corporación Punto de Vista which examines how quantitative data can be used to assess conflict related sexual violence in Colombia. Written by Francoise Roth, Tamy Guberek and Amelia Hoover Green, Using Quantitative Data to Assess Conflict-Related Sexual Violence in Colombia: Challenges and Opportunities notes that sexual violations ...
Remembering Scott Weikart
HRDAG’s core values all have a connection to Scott Weikart, 1951–2023.
The Day We Fight Back
Today, February 11, is the day of national protests against the National Security Administration.
The critical threat is mass surveillance. In the words of The Day We Fight Back, “Together we will push back against powers that seek to observe, collect, and analyze our every digital action. Together, we will make it clear that such behavior is not compatible with democratic governance. Together, if we persist, we will win this fight.” (more…)
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.
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.