Fairness

1141 papers with code • 3 benchmarks • 20 datasets

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Most implemented papers

Towards Realistic Individual Recourse and Actionable Explanations in Black-Box Decision Making Systems

carla-recourse/CARLA 22 Jul 2019

We then provide a mechanism to generate the smallest set of changes that will improve an individual's outcome.

Fairness without Demographics through Adversarially Reweighted Learning

google-research/google-research NeurIPS 2020

Much of the previous machine learning (ML) fairness literature assumes that protected features such as race and sex are present in the dataset, and relies upon them to mitigate fairness concerns.

A comparative study of fairness-enhancing interventions in machine learning

algofairness/fairness-comparison 13 Feb 2018

Concretely, we present the results of an open benchmark we have developed that lets us compare a number of different algorithms under a variety of fairness measures, and a large number of existing datasets.

Classification with Fairness Constraints: A Meta-Algorithm with Provable Guarantees

vijaykeswani/FairClassification 15 Jun 2018

The main contribution of this paper is a new meta-algorithm for classification that takes as input a large class of fairness constraints, with respect to multiple non-disjoint sensitive attributes, and which comes with provable guarantees.

Nuanced Metrics for Measuring Unintended Bias with Real Data for Text Classification

google/uncertainty-baselines 11 Mar 2019

Unintended bias in Machine Learning can manifest as systemic differences in performance for different demographic groups, potentially compounding existing challenges to fairness in society at large.

Fair Regression: Quantitative Definitions and Reduction-based Algorithms

Microsoft/fairlearn 30 May 2019

Our schemes only require access to standard risk minimization algorithms (such as standard classification or least-squares regression) while providing theoretical guarantees on the optimality and fairness of the obtained solutions.

Learning to Continuously Optimize Wireless Resource In Episodically Dynamic Environment

Haoran-S/ICASSP2021 16 Nov 2020

We propose to build the notion of continual learning (CL) into the modeling process of learning wireless systems, so that the learning model can incrementally adapt to the new episodes, {\it without forgetting} knowledge learned from the previous episodes.

Ditto: Fair and Robust Federated Learning Through Personalization

litian96/ditto 8 Dec 2020

Fairness and robustness are two important concerns for federated learning systems.

Federated Multi-Task Learning under a Mixture of Distributions

omarfoq/fedem NeurIPS 2021

The increasing size of data generated by smartphones and IoT devices motivated the development of Federated Learning (FL), a framework for on-device collaborative training of machine learning models.

FreeMatch: Self-adaptive Thresholding for Semi-supervised Learning

torchssl/torchssl 15 May 2022

Semi-supervised Learning (SSL) has witnessed great success owing to the impressive performances brought by various methods based on pseudo labeling and consistency regularization.