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The data science field is always changing, which means that I'll always be learning.
Margot is a professor in the Department of Energy Resources Engineering at Stanford University, interested in computer simulation and mathematical analysis of engineering processes.
There may have been more undocumented World War II-era Korean "comfort women" than known.
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.
It could make sense to use Rust as a data journalist for in-browser computations, and other thoughts from RustConf.
Una investigación de Dejusticia y Human Rights Data Analysis Group concluyó que hay un subconteo en los asesinatos de líderes sociales en Colombia. Es decir, que el aumento de estos crímenes en 2016 y 2017 podría ser incluso mayor al reportado por las organizaciones y por las cifras oficiales.
Patrick Ball and Michael Reed Hurtado. 2016. El registro y la medición de la criminalidad. El problema de los datos faltantes y el uso de la ciencia para producir estimaciones en relación con el homicidio en Colombia, demostrado a partir de un ejemplo: el departamento de Antioquia (2003-2011). Revista Criminalidad, 58 (1): 9-23.
Patrick Ball and Michael Reed Hurtado. 2016. El registro y la medición de la criminalidad. El problema de los datos faltantes y el uso de la ciencia para producir estimaciones en relación con el homicidio en Colombia, demostrado a partir de un ejemplo: el departamento de Antioquia (2003-2011). Revista Criminalidad, 58 (1): 9-23. Criminality registration and measurement. The problem of missing data, and the use of science to produce estimations relating to homicide in Colombia, as demonstrated with an example from one of its administrative and political divisions: the Department of Antioquia (2003-2011).
Text in English
Assessing Claims of Declining Lethal Violence
In March 2007, HRDAG published a study entitled "Assessing Claims of Declining Lethal Violence in Colombia." The authors of this report evaluated assertions that violence in Colombia declined after the demobilization of paramilitaries. They showed that these claims rest both on the overinterpretation of unadjusted data and on unsound causal inferences. The authors concluded that multiple data sources are needed to estimate the true rates of violence in Colombia after demobilization and they suggest avenues for further research.
This research paper is also available in Spanish at ...
In our database deduplication work, we’re trying to figure out which records refer to the same person, and which other records refer to different people.
We write software that looks at tens of millions of pairs of records. We calculate a model that assigns each pair of records a probability that the pair of records refers to the same person. This step is called pairwise classification.
However, there may be more than just one pair of records that refer to the same person. Sometimes three, four, or more reports of the same death are recorded.
So once we have all the pairs classified, we need to decide which groups of records refer to the ...
“In 2016, two researchers, the statistician Kristian Lum and the political scientist William Isaac, set out to measure the bias in predictive policing algorithms. They chose as their example a program called PredPol. … Lum and Isaac faced a conundrum: if official data on crimes is biased, how can you test a crime prediction model? To solve this technique, they turned to a technique used in statistics and machine learning called the synthetic population.”
Using large language models for structured information extraction from the Innocence Project New Orleans' wrongful conviction case files.
Exoneration documents, secured during legal proceedings that aim to right the wrongs of justice, are invaluable for understanding wrongful convictions. They cast a spotlight on law enforcement actions, revealing systemic challenges. Yet, finding and leveraging usable information within these collections remains a formidable task for researchers and advocates, due to their volume and unstructured heterogeneity.
This post introduces the methodology of the Innocence Discovery Lab, a collaboration between Innoce...
We’ve
built a model for estimating the true number of positives, using what we have determined to be the most reliable datasets—deaths.
Michelle spent a weekend in Toronto, Canada, reaching out to the community at TamilFest, where she and a colleague invited people to sit down and talk.
ITJP and HRDAG are urging groups inside and outside Sri Lanka to share existing casualty lists.
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 ...
How might we learn what we don’t know? HRDAG associate Christine Grillo hits the wayback machine and recalls her first exposure to People Against Bad Things, ideas about bias and correlation versus causation, and truth.
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.
I have made it my personal objective to amplify HRDAG's message of being extra careful and scientifically rigorous with human rights data.
Some of the earliest large-scale human rights information projects happened in El Salvador. One was developed by Patrick Ball at the Salvadoran non-governmental Human Rights Commission, also known as Comision de Derechos Humanos de El Salvador (CDHES-ng). Between 1977 and 1990, more than 9,000 testimonies were taken in an effort to document the nature and scope of the bloody conflict between the army and the Farabundo Marti National Liberation Front (FMLN). Starting in 1991, Patrick worked with CDHES staff to organize the information in an early computer database. They linked reported human rights violations with the career structures of individual ...
Last month Significance magazine published an article on the topic of predictive policing and police bias, which I co-authored with William Isaac. Since then, we've published a blogpost about it and fielded a few recurring questions. Here they are, along with our responses.
Do your findings still apply given that PredPol uses crime reports rather than arrests as training data?
Because this article was meant for an audience that is not necessarily well-versed in criminal justice data and we were under a strict word limit, we simplified language in describing the data. The data we used is a version of the Oakland Police Department’s crime report...
