arXiv research

DeepEthnic classifies faces into ethnic groups with high accuracy.

Paper proposes robust method to detect risk heterogeneity across ethnic groups.

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…

This research quantifies cross-sectoral inequalities using latent class analysis.

Novel approach for robust domain generalization in health studies.

The study uses transfer learning to compare surgical outcomes across racial/ethnic subgroups.

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…

Extends multivariate regression for tensor-variate data, identifying brain regions and facial characteristics.

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…

Introduces MPR to measure and optimize representation across intersectional groups in retrieval.

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…

The study examines how social biases are reinforced in machine learning models used for credit scoring.

When the performance of a machine learning model varies over groups defined by sensitive attributes (e.g., gender or ethnicity), the performance disparity can be expressed in terms of the probability distributions of the input and output variables over each group. In this paper, we exploit this fact to reduce the dispa…

New fair regression method improves fairness in chronic kidney disease classification.

New model predicts multiple outputs with missing labels.

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…

Recent work shows unequal performance of commercial face classification services in the gender classification task across intersectional groups defined by skin type and gender. Accuracy on dark-skinned females is significantly worse than on any other group. In this paper, we conduct several analyses to try to uncover t…

New dataset for evaluating speech recognition fairness across demographics.

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 …

A central goal of algorithmic fairness is to reduce bias in automated decision making. An unavoidable tension exists between accuracy gains obtained by using sensitive information (e.g., gender or ethnic group) as part of a statistical model, and any commitment to protect these characteristics. Often, due to biases pre…

BAICS identifies best arm with fairness constraints on subpopulations.

Bayesian method models binary response and covariates for two groups, estimating causal relationships.

New deep Cox mixture model improves survival analysis performance.

Study examines biases in clinical word embeddings, revealing performance gaps across groups.

Paper protects privacy and fairness in deep learning models.

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 …

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…

Research aims to ensure fair classification across explicit and implicit sensitive features.

This research compares two encoding methods for categorical attributes in machine learning, affecting model fairness.

A fair policy for hiring candidates from different groups is proposed in a linear contextual bandit problem.

Improved surname geocoding and name supplements enhance race imputation accuracy.

As virtually all aspects of our lives are increasingly impacted by algorithmic decision making systems, it is incumbent upon us as a society to ensure such systems do not become instruments of unfair discrimination on the basis of gender, race, ethnicity, religion, etc. We consider the problem of determining whether th…

MCRAGE generates synthetic data to balance healthcare datasets.

FairCal improves face verification accuracy while making results fairer.

Proposes a fair classification model using robust optimization.

Propensity score matching improves fairness in machine learning models.

What does it mean for an algorithm to be biased? In U.S. law, unintentional bias is encoded via disparate impact, which occurs when a selection process has widely different outcomes for different groups, even as it appears to be neutral. This legal determination hinges on a definition of a protected class (ethnicity, g…

Improved race prediction model outperforms existing methods.

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…

Study highlights fairness issues in travel behavior prediction models.

TaCo prevents non-linear classifiers from detecting sensitive attributes.

Proposes a new method for fairness in machine learning with multiple protected attributes.

With the rise of social media, people can now form relationships and communities easily regardless of location, race, ethnicity, or gender. However, the power of social media simultaneously enables harmful online behavior such as harassment and bullying. Cyberbullying is a serious social problem, making it an important…

We demonstrate that effortlessly accessible digital records of behavior such as Facebook Likes can be obtained and utilized to automatically distinguish a wide range of highly delicate personal traits including: life satisfaction, cultural ethnicity, political views, age, gender and personality traits. The analysis pre…

In this paper, we consider the problem of predicting demographics of geographic units given geotagged Tweets that are composed within these units. Traditional survey methods that offer demographics estimates are usually limited in terms of geographic resolution, geographic boundaries, and time intervals. Thus, it would…

Proposes a method to increase diversity without sacrificing meritocracy.

This survey outlines methods to ensure fairness in machine learning.