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The Rafto Prize 2021 to Human Rights Data Analysis Group
“The Rafto Prize 2021 is awarded to the Human Rights Data Analysis Group (HRDAG) for their wide-reaching documentation of grave human rights abuses. By using statistics and data science they uncover large-scale human rights violations that might otherwise go undetected. This novel approach has enabled courts to bring perpetrators to justice and given closure to affected victims and their families. HRDAG represents a new generation of human rights defenders that advances the enforcement of human rights globally.”
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.
Weapons of Math Destruction
Weapons of Math Destruction: invisible, ubiquitous algorithms are ruining millions of lives. Excerpt:
As Patrick once explained to me, you can train an algorithm to predict someone’s height from their weight, but if your whole training set comes from a grade three class, and anyone who’s self-conscious about their weight is allowed to skip the exercise, your model will predict that most people are about four feet tall. The problem isn’t the algorithm, it’s the training data and the lack of correction when the model produces erroneous conclusions.
All the Dead We Cannot See
Ball, a statistician, has spent the last two decades finding ways to make the silence speak. He helped pioneer the use of formal statistical modeling, and, later, machine learning—tools more often used for e-commerce or digital marketing—to measure human rights violations that weren’t recorded. In Guatemala, his analysis helped convict former dictator General Efraín Ríos Montt of genocide in 2013. It was the first time a former head of state was found guilty of the crime in his own country.
A look at the top contenders for the 2022 Nobel Peace Prize
The Washington Post’s Paul Schemm recognized HRDAG’s work in Syria, in the category of research and activism. “HRDAG gained renown at the start of the war, when it was one of the few organizations that tried to put a number on the war’s enormous toll in Syrian lives.”
What HBR Gets Wrong About Algorithms and Bias
“Kristian Lum… organized a workshop together with Elizabeth Bender, a staff attorney for the NY Legal Aid Society and former public defender, and Terrence Wilkerson, an innocent man who had been arrested and could not afford bail. Together, they shared first hand experience about the obstacles and inefficiencies that occur in the legal system, providing valuable context to the debate around COMPAS.”
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.
Trump’s “extreme-vetting” software will discriminate against immigrants “Under a veneer of objectivity,” say experts
Kristian Lum, lead statistician at the Human Rights Data Analysis Group (and letter signatory), fears that “in order to flag even a small proportion of future terrorists, this tool will likely flag a huge number of people who would never go on to be terrorists,” and that “these ‘false positives’ will be real people who would never have gone on to commit criminal acts but will suffer the consequences of being flagged just the same.”
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The ghost in the machine
“Every kind of classification system – human or machine – has several kinds of errors it might make,” [Patrick Ball] says. “To frame that in a machine learning context, what kind of error do we want the machine to make?” HRDAG’s work on predictive policing shows that “predictive policing” finds patterns in police records, not patterns in occurrence of crime.
Megan Price: Life-Long ‘Math Nerd’ Finds Career in Social Justice
“I was always a math nerd. My mother has a polaroid of me in the fourth grade with my science fair project … . It was the history of mathematics. In college, I was a math major for a year and then switched to statistics.
I always wanted to work in social justice. I was raised by hippies, went to protests when I was young. I always felt I had an obligation to make the world a little bit better.”
Cifra de líderes sociales asesinados es más alta: Dejusticia
Contrario a lo que se puede pensar, los datos oficiales sobre líderes sociales asesinados no necesariamente corresponden a la realidad y podría haber mucha mayor victimización en las regiones golpeadas por este flagelo, según el más reciente informe del Centro de Estudios de Justicia, Derecho y Sociedad (Dejusticia) en colaboración con el Human Rights Data Analysis Group.
Existe la posibilidad de que no se estén documentando todos los asesinatos contra líderes sociales
En ocasiones, las discusiones sobre ese fenómeno se centran más sobre cuál es la cifra real, mientras que el diagnóstico es el mismo: en las regiones la violencia no cede y no se avizoran políticas efectivas para ponerle fin. En medio de este complejo panorama, el Centro de Estudios de Derecho, Justicia y Sociedad (Dejusticia) y el Human Rights Data Analysis Group, publicaron este miércoles la investigación Asesinatos de líderes sociales en Colombia en 2016–2017: una estimación del universo.
Hunting for Mexico’s mass graves with machine learning
“The model uses obvious predictor variables, Ball says, such as whether or not a drug lab has been busted in that county, or if the county borders the United States, or the ocean, but also includes less-obvious predictor variables such as the percentage of the county that is mountainous, the presence of highways, and the academic results of primary and secondary school students in the county.”