577 results for search: %E6%80%8E%E6%A0%B7%E8%A7%A3%E5%86%B3%E5%BC%80%E6%9C%BA%E6%97%B6%E6%9B%B4%E6%96%B0%E7%9A%84%E9%97%AE%E9%A2%98-%E3%80%90%E2%9C%94%EF%B8%8F%E6%8E%A8%E8%8D%90KK37%C2%B7CC%E2%9C%94%EF%B8%8F%E3%80%91-%E6%B3%95%E6%B2%BB%E5%BB%BA%E8%AE%BE%E5%B7%A5%E4%BD%9C%E4%BC%9A%E8%AE%AE%E8%AE%B0%E5%BD%95-%E6%80%8E%E6%A0%B7%E8%A7%A3%E5%86%B3%E5%BC%80%E6%9C%BA%E6%97%B6%E6%9B%B4%E6%96%B0%E7%9A%84%E9%97%AE%E9%A2%98zxdz8-%E3%80%90%E2%9C%94%EF%B8%8F%E6%8E%A8%E8%8D%90KK37%C2%B7CC%E2%9C%94%EF%B8%8F%E3%80%91-%E6%B3%95%E6%B2%BB%E5%BB%BA%E8%AE%BE%E5%B7%A5%E4%BD%9C%E4%BC%9A%E8%AE%AE%E8%AE%B0%E5%BD%95trau-%E6%80%8E%E6%A0%B7%E8%A7%A3%E5%86%B3%E5%BC%80%E6%9C%BA%E6%97%B6%E6%9B%B4%E6%96%B0%E7%9A%84%E9%97%AE%E9%A2%98s9y9p-%E6%B3%95%E6%B2%BB%E5%BB%BA%E8%AE%BE%E5%B7%A5%E4%BD%9C%E4%BC%9A%E8%AE%AE%E8%AE%B0%E5%BD%95lwom/feed/rss2/privacy
HRDAG Offers New R Package – dga
100 Women in AI Ethics
We live in very challenging times. The pervasiveness of bias in AI algorithms and autonomous “killer” robots looming on the horizon, all necessitate an open discussion and immediate action to address the perils of unchecked AI. The decisions we make today will determine the fate of future generations. Please follow these amazing women and support their work so we can make faster meaningful progress towards a world with safe, beneficial AI that will help and not hurt the future of humanity.
53. Kristian Lum @kldivergence
How Machine Learning Protects Whistle-Blowers in Staten Island
Megan Price Elected Board Member of Tor Project
Courts and police departments are turning to AI to reduce bias, but some argue it’ll make the problem worse
Kristian Lum: “The historical over-policing of minority communities has led to a disproportionate number of crimes being recorded by the police in those locations. Historical over-policing is then passed through the algorithm to justify the over-policing of those communities.”
Beautiful game, ugly truth?
Megan Price (2022). Beautiful game, ugly truth? Significance, 19: 18-21. December 2022. © The Royal Statistical Society. https://doi.org/10.1111/1740-9713.01702
The Bigness of Big Data: samples, models, and the facts we might find when looking at data
Patrick Ball. 2015. The Bigness of Big Data: samples, models, and the facts we might find when looking at data. In The Transformation of Human Rights Fact-Finding, ed. Philip Alston and Sarah Knuckey. New York: Oxford University Press. ISBN: 9780190239497. © The Oxford University Press. All rights reserved.
Syria’s status, the migrant crisis and talking to ISIS
In this week’s “Top Picks,” IRIN interviews HRDAG executive director Patrick Ball about giant data sets and whether we can trust them. “No matter how big it is, data on violence is always partial,” he says.
Guatemala 2011 – Developing Sampling Methods to Help Convict Perpetrators
Haiti
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?’”
HRDAG Welcomes New Data Science Fellow
HRDAG Names New Board Member William Isaac
Truth Commissioner
From the Guatemalan military to the South African apartheid police, code cruncher Patrick Ball singles out the perpetrators of political violence.
Data ‘hashing’ improves estimate of the number of victims in databases
But while HRDAG’s estimate relied on the painstaking efforts of human workers to carefully weed out potential duplicate records, hashing with statistical estimation proved to be faster, easier and less expensive. The researchers said hashing also had the important advantage of a sharp confidence interval: The range of error is plus or minus 1,772, or less than 1 percent of the total number of victims.
“The big win from this method is that we can quickly calculate the probable number of unique elements in a dataset with many duplicates,” said Patrick Ball, HRDAG’s director of research. “We can do a lot with this estimate.”
Who Did What to Whom? Planning and Implementing a Large Scale Human Rights Data Project
Patrick Ball. Who Did What to Whom? Planning and Implementing a Large Scale Human Rights Data Project. © 1996 American Association for the Advancement of Science.
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