714 results for search: %EB%82%A8%EA%B5%AC%ED%9C%B4%EA%B2%8C%ED%85%94%E3%81%ACjusobot%E3%80%81%EF%BD%830m%E2%99%99%EB%82%A8%EA%B5%AC%EA%B1%B4%EB%A7%88%E2%99%A9%EB%82%A8%EA%B5%AC%EC%97%85%EC%86%8C%E2%9C%88%EB%82%A8%EA%B5%AC%EB%A6%BD%EB%B0%A9%20%EB%82%A8%EA%B5%AC%EB%8B%AC%EB%A6%BC/feed/content/colombia/SV-report_2011-04-26.pdf
As we get ready to begin our fourth year as an independent nonprofit, we are, as always, indebted to our Advisory Board and to our funders for their support and vision. We’re finishing up a busy year that took us to Dakar (for the trial of former Chadian dictator Hissène Habré), Pristina (for the release of the Kosovo Memory Book), Colombia (for work on a book about the Guatemalan Police Archives), and kept us busy here at home working on police violence statistics. But one of our biggest victories has been to score a new, talented, wise Advisory Board member—Michael Bear Kleinman, whom we first met when he was working with Humanity United.
...
In her work on statistical issues in criminal justice, Lum has studied uses of predictive policing—machine learning models to predict who will commit future crime or where it will occur. In her work, she has demonstrated that if the training data encodes historical patterns of racially disparate enforcement, predictions from software trained with this data will reinforce and—in some cases—amplify this bias. She also currently works on statistical issues related to criminal “risk assessment” models used to inform judicial decision-making. As part of this thread, she has developed statistical methods for removing sensitive information from training data, guaranteeing “fair” predictions with respect to sensitive variables such as race and gender. Lum is active in the fairness, accountability, and transparency (FAT) community and serves on the steering committee of FAT, a conference that brings together researchers and practitioners interested in fairness, accountability, and transparency in socio-technical systems.
One of the researchers, a Michigan State PhD candidate named William Isaac, had not previously heard of New Orleans’ partnership with Palantir, but he recognized the data-mapping model at the heart of the program. “I think the data they’re using, there are serious questions about its predictive power. We’ve seen very little about its ability to forecast violent crime,” Isaac said.
On November 26, HRDAG colleague Anita Gohdes was awarded the German Dissertation Prize for the Social Sciences. The patron of the prize is the President of the German Parliament, Norbert Lammert, who presented Anita with the award.
Anita’s dissertation, “Repression 2.0: The Internet in the War Arsenal of Modern Dictators,” investigates the role played by social media networks in modern dictatorships, such as President Assad’s regime in Syria. On one hand, Anita argues, social media can help opposition groups to organize more effectively, but on the other hand, the same networks allow regimes to monitor and manipulate the population. ...
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.”
Violence against women in all its forms is a human rights violation. Most of our HRDAG colleagues are women, and for us, unfortunately, recent campaigns such as #metoo are unsurprising.
In this
Granta article, HRDAG explains that neither the infectiousness nor the deadliness of the disease is set in stone.
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 ...
As a woman, mother and sociologist who is curious about the patterns of our political past in Guatemala, I feel privileged to know and work with the HRDAG team. Collaborating and learning from people like Patrick, Megan, Suzanne, Beatriz and Tamy has been an invaluable gift. I have discovered many things, both human and academic. For example, I’ve learned new ways of seeing what seemed everyday and simple, to discover that not only do the social sciences and statistics work hand in hand, but that they are critical for understanding Guatemala’s reality.
Twenty years ago, on 29 December, 1996, Guatemala made history by signing the Guatemala Peace ...
Much of the work we do at HRDAG involves estimating the number of undocumented deaths using a statistical technique called multiple systems estimation (MSE, described in more detail here). One of our goals is to make this class of methods more broadly available to human rights researchers. In particular, we are finding that Bayesian approaches are extremely valuable for MSE. Accordingly, we are pleased to offer a new R package called dga (“decomposable graphs approach”) that performs Bayesian model averaging for MSE.
The main function in this package implements a model created by David Madigan and Jeremy York. This model was designed to ...
The interview poses questions about Lum's focus on artificial intelligence and its impact on predictive policing and sentencing programs.
Margot is a professor in the Department of Energy Resources Engineering at Stanford University, interested in computer simulation and mathematical analysis of engineering processes.
In this story about how data are transforming the nonprofit world, Patrick Ball is quoted. Here's an excerpt:
"Data can have a profound impact on certain problems, but nonprofits are kidding themselves if they think the data techniques used by corporations can be applied wholesale to social problems," says Patrick Ball, head of the nonprofit Human Rights Data Analysis Group.
Companies, he says, maintain complete data sets. A business knows every product it made last year, when it sold, and to whom. Charities, he says, are a different story.
"If you're looking at poverty or trafficking or homicide, we don't have all the data, and we're not going to," ...
Kristian Lum, lead statistician at HRDAG | Predictive Policing: Bias In, Bias Out | 56 mins
Help Us Advance Justice And Human Rights
Your donations enable HRDAG to use data science and help our partners
answer important questions about human rights and patterns of mass violence.
Or Write a Check
If you prefer to donate by check, please make it payable to: “Community Partners for HRDAG”
Mail it to:
Community Partners
P. O. Box 741265
Los Angeles, CA 90074-1265
To speak with the researchers at HRDAG, please fill out the form below.
You can search our Press Room by keyword or by year.
The Rafto Foundation, an international human rights organization, has bestowed the 2021 Rafto Prize to HRDAG for its distinguished work defending human rights and democracy.
