A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Paper uses conformal prediction sets to make criminal justice risk assessments fairer.
Objectives: Discussions of fairness in criminal justice risk assessments typically lack conceptual precision. Rhetoric too often substitutes for careful analysis. In this paper, we seek to clarify the tradeoffs between different kinds of fairness and between fairness and accuracy. Methods: We draw on the existing liter…
Black box machine learning models are currently being used for high stakes decision-making throughout society, causing problems throughout healthcare, criminal justice, and in other domains. People have hoped that creating methods for explaining these black box models will alleviate some of these problems, but trying t…
Novel framework for Bayesian neural networks incorporating task-specific constraints.
Discussing the need for explainable AI in various fields.
Whereas previous post-processing approaches for increasing the fairness of predictions of biased classifiers address only group fairness, we propose a method for increasing both individual and group fairness. Our novel framework includes an individual bias detector used to prioritize data samples in a bias mitigation a…
Many machine learning systems make extensive use of large amounts of data regarding human behaviors. Several researchers have found various discriminatory practices related to the use of human-related machine learning systems, for example in the field of criminal justice, credit scoring and advertising. Fair machine le…
Method interprets deep learning models using topological data analysis.
New framework makes ML methods compliant with regulations.
Recent attempts to achieve fairness in predictive models focus on the balance between fairness and accuracy. In sensitive applications such as healthcare or criminal justice, this trade-off is often undesirable as any increase in prediction error could have devastating consequences. In this work, we argue that the fair…
Neural network model predicts alternating event-free periods.
Intersectionality is a framework that analyzes how interlocking systems of power and oppression affect individuals along overlapping dimensions including race, gender, sexual orientation, class, and disability. Intersectionality theory therefore implies it is important that fairness in artificial intelligence systems b…
Predictive modeling is increasingly being employed to assist human decision-makers. One purported advantage of replacing human judgment with computer models in high stakes settings-- such as sentencing, hiring, policing, college admissions, and parole decisions-- is the perceived "neutrality" of computers. It is argued…
New approach to fairness in machine learning through stochastic optimization.
Machine learning predicts criminal networks' missing partnerships and future behavior.
Systematic discriminatory biases present in our society influence the way data is collected and stored, the way variables are defined, and the way scientific findings are put into practice as policy. Automated decision procedures and learning algorithms applied to such data may serve to perpetuate existing injustice or…
Successful attempts to predict judges' votes shed light into how legal decisions are made and, ultimately, into the behavior and evolution of the judiciary. Here, we investigate to what extent it is possible to make predictions of a justice's vote based on the other justices' votes in the same case. For our predictions…
Fairness is becoming a rising concern w.r.t. machine learning model performance. Especially for sensitive fields such as criminal justice and loan decision, eliminating the prediction discrimination towards a certain group of population (characterized by sensitive features like race and gender) is important for enhanci…
A new method creates simpler, more interpretable decision trees from complex ensembles.
Researchers propose a method to quantify explainability in AI systems.
Interpretable ML models predict recidivism as well as non-interpretable methods and are more fair.
Homicide mortality is a worldwide concern and has occupied the agenda of researchers and public managers. In Brazil, homicide is the third leading cause of death in the general population and the first in the 15-39 age group. In South America, Brazil has the third highest homicide mortality, behind Venezuela and Colomb…
The paper studies and mitigates accuracy disparity in regression models.
Fairness measures fail in predictive settings that intentionally shift outcomes.
Introduces Rashomon Capacity to measure predictive multiplicity in probabilistic classifiers.
Complex statistical machine learning models are increasingly being used or considered for use in high-stakes decision-making pipelines in domains such as financial services, health care, criminal justice and human services. These models are often investigated as possible improvements over more classical tools such as r…
Variable importance is central to scientific studies, including the social sciences and causal inference, healthcare, and other domains. However, current notions of variable importance are often tied to a specific predictive model. This is problematic: what if there were multiple well-performing predictive models, and …
Machine learning algorithms are now frequently used in sensitive contexts that substantially affect the course of human lives, such as credit lending or criminal justice. This is driven by the idea that `objective' machines base their decisions solely on facts and remain unaffected by human cognitive biases, discrimina…
Risk scores are simple classification models that let users make quick risk predictions by adding and subtracting a few small numbers. These models are widely used in medicine and criminal justice, but are difficult to learn from data because they need to be calibrated, sparse, use small integer coefficients, and obey …
UDJ-FL framework achieves multiple distributive justice-based fairness metrics in federated learning.
Paper shows LIME and SHAP explanations can be fooled by adversarial attacks.
The paper examines how machine learning tools in justice settings can unfairly affect different racial groups.
Where machine-learned predictive risk scores inform high-stakes decisions, such as bail and sentencing in criminal justice, fairness has been a serious concern. Recent work has characterized the disparate impact that such risk scores can have when used for a binary classification task. This may not account, however, fo…
The paper tackles fairness in machine learning by modeling latent unbiased labels.
The paper explores fairness metrics in automated decision-making and their limitations.
Noise increases the Rashomon ratio, leading simpler models to perform similarly to complex ones.
FairPOT balances fairness and AUC performance by selectively transforming risk scores.
New model detects hidden group structures in criminal networks.
The increasing impact of algorithmic decisions on people's lives compels us to scrutinize their fairness and, in particular, the disparate impacts that ostensibly-color-blind algorithms can have on different groups. Examples include credit decisioning, hiring, advertising, criminal justice, personalized medicine, and t…
Proposes a falsification framework to test algorithmic discriminant validity.
Mixed-integer optimization improves fairness and transparency in machine learning models.
Improves pre-trial risk assessments by making them safer without changing existing rules.
Proposes a test to ensure predictive algorithms predict intended outcomes better than unintended ones.
Develops causal framework for fair survival analysis in healthcare.
Algorithmic risk assessments are increasingly used to help humans make decisions in high-stakes settings, such as medicine, criminal justice and education. In each of these cases, the purpose of the risk assessment tool is to inform actions, such as medical treatments or release conditions, often with the aim of reduci…
Study disrupts Sicilian Mafia networks using data analysis.
New digital currency aims for equal wealth distribution.
Develops a method to quantify racial bias in law enforcement systems.