REVIEW 6 cited by
Bias Mitigation for Machine Learning Classifiers: A Comprehensive Survey
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
Bias Mitigation for Machine Learning Classifiers: A Comprehensive Survey
read the original abstract
This paper provides a comprehensive survey of bias mitigation methods for achieving fairness in Machine Learning (ML) models. We collect a total of 341 publications concerning bias mitigation for ML classifiers. These methods can be distinguished based on their intervention procedure (i.e., pre-processing, in-processing, post-processing) and the technique they apply. We investigate how existing bias mitigation methods are evaluated in the literature. In particular, we consider datasets, metrics and benchmarking. Based on the gathered insights (e.g., What is the most popular fairness metric? How many datasets are used for evaluating bias mitigation methods?), we hope to support practitioners in making informed choices when developing and evaluating new bias mitigation methods.
Forward citations
Cited by 6 Pith papers
-
Unmasking LAION-5B: Age, Gender, Race, and Emotion Biases in Large-Scale Image Datasets
Empirical audit of LAION-2B-en and LAION-2B-multi finds overrepresentation of young adults, White people, and males plus stereotypical emotion associations across two attribute classifiers.
-
Trustworthy AI: Ensuring Reliability and Accountability from Models to Agents
The thesis presents a kernel method for multiaccuracy across overlooked subpopulations, information-theoretic optimal watermarking for LLMs, and a simulator showing LLM agents outperforming humans in supply chains whi...
-
FairSelect: A Systematic Evaluation of Multi-Level and Intersectional Algorithmic Fairness
FairSelect systematically evaluates single and multi-level fairness interventions with intersectional metrics, finding non-additive, context-dependent effects on synthetic bias tests and a real AF stroke-risk task.
-
Evaluating Intersectional Fairness across Clinical Machine Learning Use Cases using Fairlogue and the All of Us Research Program
Intersectional fairness audits of two clinical models on All of Us found larger subgroup gaps than single-axis checks, yet counterfactuals suggested most gaps matched randomized group membership.
-
FairLogue: A Toolkit for Intersectional Fairness Analysis in Clinical Machine Learning Models
FairLogue provides modular tools to quantify intersectional fairness gaps in clinical ML using extended demographic parity, equalized odds, and counterfactual methods, shown on a glaucoma surgery prediction task from ...
-
Evaluating Intersectional Fairness across Clinical Machine Learning Use Cases using Fairlogue and the All of Us Research Program
FairLogue shows that intersectional disparities in two clinical prediction tasks are largely consistent with randomized group membership.
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.