Fairness

447 papers with code • 0 benchmarks • 14 datasets

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Libraries

Use these libraries to find Fairness models and implementations

Most implemented papers

FairMOT: On the Fairness of Detection and Re-Identification in Multiple Object Tracking

ifzhang/FairMOT 4 Apr 2020

Formulating MOT as multi-task learning of object detection and re-ID in a single network is appealing since it allows joint optimization of the two tasks and enjoys high computation efficiency.

AI Fairness 360: An Extensible Toolkit for Detecting, Understanding, and Mitigating Unwanted Algorithmic Bias

IBM/AIF360 3 Oct 2018

Such architectural design and abstractions enable researchers and developers to extend the toolkit with their new algorithms and improvements, and to use it for performance benchmarking.

A Critic Evaluation of Methods for COVID-19 Automatic Detection from X-Ray Images

dragonsan17/covid_detection_from_xray 27 Apr 2020

In this paper, we compare and evaluate different testing protocols used for automatic COVID-19 diagnosis from X-Ray images in the recent literature.

Score-CAM: Score-Weighted Visual Explanations for Convolutional Neural Networks

haofanwang/Score-CAM 3 Oct 2019

Recently, increasing attention has been drawn to the internal mechanisms of convolutional neural networks, and the reason why the network makes specific decisions.

Agnostic Federated Learning

litian96/fair_flearn 1 Feb 2019

A key learning scenario in large-scale applications is that of federated learning, where a centralized model is trained based on data originating from a large number of clients.

Learning to Pivot with Adversarial Networks

glouppe/paper-learning-to-pivot NeurIPS 2017

Several techniques for domain adaptation have been proposed to account for differences in the distribution of the data used for training and testing.

Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness

algowatchpenn/GerryFair ICML 2018

We prove that the computational problem of auditing subgroup fairness for both equality of false positive rates and statistical parity is equivalent to the problem of weak agnostic learning, which means it is computationally hard in the worst case, even for simple structured subclasses.

An Empirical Study of Rich Subgroup Fairness for Machine Learning

algowatchpenn/GerryFair 24 Aug 2018

In this paper, we undertake an extensive empirical evaluation of the algorithm of Kearns et al. On four real datasets for which fairness is a concern, we investigate the basic convergence of the algorithm when instantiated with fast heuristics in place of learning oracles, measure the tradeoffs between fairness and accuracy, and compare this approach with the recent algorithm of Agarwal et al. [2018], which implements weaker and more traditional marginal fairness constraints defined by individual protected attributes.

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.

Learning Adversarially Fair and Transferable Representations

VectorInstitute/laftr ICML 2018

In this paper, we advocate for representation learning as the key to mitigating unfair prediction outcomes downstream.