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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.
About Us
Are journalists lowballing the number of Iraqi war dead?
The Columbia Journalism Review investigates the casualty count in Iraq, more than a decade after the U.S. invasion. HRDAG executive director Patrick Ball is quoted. “IBC is very good at covering the bombs that go off in markets,” said Patrick Ball, an analyst at the Human Rights Data Analysis Group who says his whole career is to study “people being killed.” But quiet assassinations and military skirmishes away from the capital often receive little or no media attention.
Sierra Leone
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.”
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?’”
How do epidemiologists know how many people will get Covid-19?
Patrick Ball (2020). How do epidemiologists know how many people will get Covid-19? Significance. 09 April 2020. © 2020 The Royal Statistical Society.
Estimating the human toll in Syria
Megan Price (2017). Estimating the human toll in Syria. Nature. 8 February 2017. © 2017 Macmillan Publishers Limited, part of Springer Nature. All rights reserved. Nature Human Behaviour. ISSN 2397-3374.
The Data Scientist Helping to Create Ethical Robots
Kristian Lum is focusing on artificial intelligence and the controversial use of predictive policing and sentencing programs.
What’s the relationship between statistics and AI and machine learning?
AI seems to be a sort of catchall for predictive modeling and computer modeling. There was this great tweet that said something like, “It’s AI when you’re trying to raise money, ML when you’re trying to hire developers, and statistics when you’re actually doing it.” I thought that was pretty accurate.
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.”
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.”
Humanitarian Statistics
In late 2006, a statistical study of deaths that occurred after the invasion of Iraq ignited a storm of controversy. This Lancet study estimated that more than 650,000 additional Iraqis died during the invasion than would have at pre-invasion death rates, a vastly higher estimate than any previous. But in January, a World Health Organization study placed the number at about 150,000.