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.
The study uses transfer learning to compare surgical outcomes across racial/ethnic subgroups.
To answer questions about racial inequality and fairness, we often need a way to infer race and ethnicity from names. One way to infer race and ethnicity from names is by relying on the Census Bureau's list of popular last names. The list, however, suffers from at least three limitations: 1. it only contains last names…
We consider training machine learning models that are fair in the sense that their performance is invariant under certain sensitive perturbations to the inputs. For example, the performance of a resume screening system should be invariant under changes to the gender and/or ethnicity of the applicant. We formalize this …
Improved surname geocoding and name supplements enhance race imputation accuracy.
The Infant Mortality Rate (IMR) is the number of infants per 1000 that do not survive until their first birthday. It is an important metric providing information about infant health but it also measures the society's general health status. Despite the high level of prosperity in the U.S.A., the country's IMR is higher …
Study shows how algorithmic prediction affects US housing market, reducing racial wealth disparities.
Controversies around race and machine learning have sparked debate among computer scientists over how to design machine learning systems that guarantee fairness. These debates rarely engage with how racial identity is embedded in our social experience, making for sociological and psychological complexity. This complexi…
Extends multivariate regression for tensor-variate data, identifying brain regions and facial characteristics.
DeepEthnic classifies faces into ethnic groups with high accuracy.
Study decomposes racial healthcare disparities via shifts in mediator distributions.
The paper examines how machine learning tools in justice settings can unfairly affect different racial groups.
ProPublica's analysis of recidivism predictions produced by Correctional Offender Management Profiling for Alternative Sanctions (COMPAS) software tool for the task, has shown that the predictions were racially biased against African American defendants. We analyze the COMPAS data using a causal reformulation of the un…
Paper proposes robust method to detect risk heterogeneity across ethnic groups.
A reliable human skin detection method that is adaptable to different human skin colours and illu- mination conditions is essential for better human skin segmentation. Even though different human skin colour detection solutions have been successfully applied, they are prone to false skin detection and are not able to c…
Societal bias towards certain communities is a big problem that affects a lot of machine learning systems. This work aims at addressing the racial bias present in many modern gender recognition systems. We learn race invariant representations of human faces with an adversarially trained autoencoder model. We show that …
Study finds racial bias in pulse oximeter readings has minimal impact on ICU ventilation rates.
In this work, we investigate the importance of ethnicity in colorectal cancer survivability prediction using machine learning techniques and the SEER cancer incidence database. We compare model performances for 2-year survivability prediction and feature importance rankings between Hispanic, White, and mixed patient po…
Over the years, several studies have demonstrated that there exist significant disparities in health indicators in the United States population across various groups. Healthcare expense is used as a proxy for health in algorithms that drive healthcare systems and this exacerbates the existing bias. In this work, we foc…
Prior work finds a diversity paradox: diversity breeds innovation, and yet, underrepresented groups that diversify organizations have less successful careers within them. Does the diversity paradox hold for scientists as well? We study this by utilizing a near-population of ~1.2 million US doctoral recipients from 1977…
This research quantifies cross-sectoral inequalities using latent class analysis.
Improved image learning using elliptically contoured tensor-variate distributions.
Develops a method to quantify racial bias in law enforcement systems.
Recidivism prediction scores are used across the USA to determine sentencing and supervision for hundreds of thousands of inmates. One such generator of recidivism prediction scores is Northpointe's Correctional Offender Management Profiling for Alternative Sanctions (COMPAS) score, used in states like California and F…
Novel approach for robust domain generalization in health studies.
New dataset for evaluating speech recognition fairness across demographics.
The study examines how social biases are reinforced in machine learning models used for credit scoring.
Black women and white men have the highest income disparity in the U.S.
Author name disambiguation in bibliographic databases is the problem of grouping together scientific publications written by the same person, accounting for potential homonyms and/or synonyms. Among solutions to this problem, digital libraries are increasingly offering tools for authors to manually curate their publica…
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…
Develops causal framework for fair survival analysis in healthcare.
Develops tools to audit ML models for bias and unfairness.
New deep Cox mixture model improves survival analysis performance.
The perennial problem of "how many clusters?" remains an issue of substantial interest in data mining and machine learning communities, and becomes particularly salient in large data sets such as populational genomic data where the number of clusters needs to be relatively large and open-ended. This problem gets furthe…
The study of genetic variants can help find correlating population groups to identify cohorts that are predisposed to common diseases and explain differences in disease susceptibility and how patients react to drugs. Machine learning algorithms are increasingly being applied to identify interacting GVs to understand th…
Method debiases alternative data for fair credit underwriting.
The study tests and optimizes fairness in credit scoring models.
Two simple methods learn fair metrics from data to improve fairness in ML tasks.
Proposes a fair classification model using robust optimization.
Improved race prediction model outperforms existing methods.
New method for analyzing compositional data, addressing biases in summary statistics.
The paper tackles individual fairness in ML models, developing statistical methods to detect bias.
Linear Mixed Models (LMMs) are important tools in statistical genetics. When used for feature selection, they allow to find a sparse set of genetic traits that best predict a continuous phenotype of interest, while simultaneously correcting for various confounding factors such as age, ethnicity and population structure…
A machine learning model may exhibit discrimination when used to make decisions involving people. One potential cause for such outcomes is that the model uses a statistical proxy for a protected demographic attribute. In this paper we formulate a definition of proxy use for the setting of linear regression and present …
Automatic understanding of human affect using visual signals is a problem that has attracted significant interest over the past 20 years. However, human emotional states are quite complex. To appraise such states displayed in real-world settings, we need expressive emotional descriptors that are capable of capturing an…
TaCo prevents non-linear classifiers from detecting sensitive attributes.
SLUG method detects bias and out-of-distribution content in generative models.
Introduces privilege scores to measure and interpret protected attribute-related privilege in machine learning models.
Introduces MPR to measure and optimize representation across intersectional groups in retrieval.