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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.
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.
Improving the estimate of U.S. police killings
Cory Doctorow of Boing Boing writes about HRDAG executive director Patrick Ball and his contribution to Carl Bialik’s article about the recently released Bureau of Justice Statistics report on the number of annual police killings, both reported and unreported, in 538 Politics.
Sous la dictature d’Hissène Habré, le ridicule tuait
Patrick Ball, un expert en statistiques engagé par les Chambres africaines extraordinaires, a conclu que la « mortalité dans les prisons de la DDS fut substantiellement plus élevée que celles des pires contextes du XXe siècle de prisonniers de guerre ».
Inside the Difficult, Dangerous Work of Tallying the ISIS Death Toll
HRDAG executive director Megan Price is interviewed by Mother Jones. An excerpt: “Violence can be hidden,” says Price. “ISIS has its own agenda. Sometimes that agenda is served by making public things they’ve done, and I have to assume, sometimes it’s served by hiding things they’ve done.”
New UN report counts 191,369 Syrian-war deaths — but the truth is probably much, much worse
Amanda Taub of Vox has interviewed HRDAG executive director about the UN Office of the High Commissioner of Human Right’s release of HRDAG’s third report on reported killings in the Syrian conflict.
From the article:
Patrick Ball, Executive Director of the Human Rights Data Analysis Group and one of the report’s authors, explained to me that this new report is not a statistical estimate of the number of people killed in the conflict so far. Rather, it’s an actual list of specific victims who have been identified by name, date, and location of death. (The report only tracked violent killings, not “excess mortality” deaths from from disease or hunger that the conflict is causing indirectly.)
How statistics lifts the fog of war in Syria
Megan Price, director of research, is quoted from her Strata talk, regarding how to handle multiple data sources in conflicts such as the one in Syria. From the blogpost:
“The true number of casualties in conflicts like the Syrian war seems unknowable, but the mission of the Human Rights Data Analysis Group (HRDAG) is to make sense of such information, clouded as it is by the fog of war. They do this not by nominating one source of information as the “best”, but instead with statistical modeling of the differences between sources.”
Data Mining for Good: Thoreau Center Lunch + Learn
Core Concepts
Uncovering Police Violence in Chicago: A collaboration between HRDAG and Invisible Institute
Pulling the Plug: Network Disruptions and Violence in the Syrian Conflict
Donate with Cryptocurrency
Ouster of Guatemala’s Attorney General
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?’”