arXiv research

New model considers unfairness complaints to ensure multiple fairness criteria.

A text mining approach is proposed based on latent Dirichlet allocation (LDA) to analyze the Consumer Financial Protection Bureau (CFPB) consumer complaints. The proposed approach aims to extract latent topics in the CFPB complaint narratives, and explores their associated trends over time. The time trends will then be…

Detects systematic anomalies in consumer complaints using NLP.

The online environment has provided a great opportunity for insurance policyholders to share their complaints with respect to different services. These complaints can reveal valuable information for insurance companies who seek to improve their services; however, analyzing a huge number of online complaints is a compli…

Unstructured data refers to information that does not have a predefined data model or is not organized in a pre-defined manner. Loosely speaking, unstructured data refers to text data that is generated by humans. In after-sales service businesses, there are two main sources of unstructured data: customer complaints, wh…

A variety of methods existing for generating synthetic electronic health records (EHRs), but they are not capable of generating unstructured text, like emergency department (ED) chief complaints, history of present illness or progress notes. Here, we use the encoder-decoder model, a deep learning algorithm that feature…

Language models learn automotive complaints, improving defect detection.

Syndromic surveillance detects and monitors individual and population health indicators through sources such as emergency department records. Automated classification of these records can improve outbreak detection speed and diagnosis accuracy. Current syndromic systems rely on hand-coded keyword-based methods to parse…

Many methods have been proposed for detecting emerging events in text streams using topic modeling. However, these methods have shortcomings that make them unsuitable for rapid detection of locally emerging events on massive text streams. We describe Spatially Compact Semantic Scan (SCSS) that has been developed specif…

Discrimination via algorithmic decision making has received considerable attention. Prior work largely focuses on defining conditions for fairness, but does not define satisfactory measures of algorithmic unfairness. In this paper, we focus on the following question: Given two unfair algorithms, how should we determine…

We offer a graphical interpretation of unfairness in a dataset as the presence of an unfair causal path in the causal Bayesian network representing the data-generation mechanism. We use this viewpoint to revisit the recent debate surrounding the COMPAS pretrial risk assessment tool and, more generally, to point out tha…

New ML fairness measure excludes subjective opinions.

Develops tools to audit ML models for bias and unfairness.

Optimal LDP mechanisms reduce data unfairness in classification.

Proposes a fair classification model using robust optimization.

Optimal exit strategies of CPT gamblers in unfair gambles

Paper compares fairness measures and feature importance measures using SHAP.

We study fairness in collaborative-filtering recommender systems, which are sensitive to discrimination that exists in historical data. Biased data can lead collaborative-filtering methods to make unfair predictions for users from minority groups. We identify the insufficiency of existing fairness metrics and propose f…

Recent work in fairness in machine learning has proposed adjusting for fairness by equalizing accuracy metrics across groups and has also studied how datasets affected by historical prejudices may lead to unfair decision policies. We connect these lines of work and study the residual unfairness that arises when a fairn…

Advocates focusing on utility functions to avoid unfair outcomes.

Paper defines and solves a problem in representation learning to ensure fairness with high confidence.

This work uncovers how model and data biases interact to cause unfairness in fraud detection.

Framework improves fairness in machine learning models using adversarial techniques.

New method detects and prevents unfairness in few-shot regression models.

The paper explores fair predictors in supervised learning using IPMs and Kolmogorov distance.

The paper critiques ε-fairness, showing it can lead to unfair outcomes and proposes a utility-based approach.

FPFL mitigates unfairness in private federated learning.

We present a new approach for mitigating unfairness in learned classifiers. In particular, we focus on binary classification tasks over individuals from two populations, where, as our criterion for fairness, we wish to achieve similar false positive rates in both populations, and similar false negative rates in both po…

Formulates LGFO to measure fair ML systems using legal signals.

Local discovery method uncovers direct unfairness in complex systems.

Visual summarization of clinical data collected on patients contained within the electronic health record (EHR) may enable precise and rapid triage at the time of patient presentation to an emergency department (ED). The triage process is critical in the appropriate allocation of resources and in anticipating eventual …

We extend the fair machine learning literature by considering the problem of proportional centroid clustering in a metric context. For clustering $n$ points with $k$ centers, we define fairness as proportionality to mean that any $n/k$ points are entitled to form their own cluster if there is another center that is clo…

Adversarial training can lead to unfair accuracy disparities between different groups.

We consider the problem of learning fair decision systems in complex scenarios in which a sensitive attribute might affect the decision along both fair and unfair pathways. We introduce a causal approach to disregard effects along unfair pathways that simplifies and generalizes previous literature. Our method corrects …

Black-box explanation is the problem of explaining how a machine learning model -- whose internal logic is hidden to the auditor and generally complex -- produces its outcomes. Current approaches for solving this problem include model explanation, outcome explanation as well as model inspection. While these techniques …

Automated data-driven decision making systems are increasingly being used to assist, or even replace humans in many settings. These systems function by learning from historical decisions, often taken by humans. In order to maximize the utility of these systems (or, classifiers), their training involves minimizing the e…

This paper examines AI and ML bias and fairness issues.

Proposes a method to learn fair classifiers without restrictive assumptions.

Paper tackles unfair advantages in DARTS, presenting Fair DARTS to improve neural architecture search.

New method uses causal thinking to make AI fairer decisions.

Fairness measures fail in predictive settings that intentionally shift outcomes.

Doubly fair dynamic pricing ensures equal prices for different groups over time.

New method detects and mitigates historical bias in data.

This paper examines unfair trading practices in NFT markets.

New clustering method considers causal fairness to avoid bias.

The paper addresses fairness issues in screening classifiers, proposing within-group monotonicity to avoid unfair treatment of qualified candidates.

Learning data representations that are transferable and are fair with respect to certain protected attributes is crucial to reducing unfair decisions while preserving the utility of the data. We propose an information-theoretically motivated objective for learning maximally expressive representations subject to fairnes…

Investigates fairness in pipeline models where individuals may drop out.