Fairness
1172 papers with code • 3 benchmarks • 20 datasets
Benchmarks
These leaderboards are used to track progress in Fairness
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Libraries
Use these libraries to find Fairness models and implementations
Netflix Prize
UTKFace
MORPH
FairFace
WinoBias
GVGAI
HELP
MIAP
Latest papers with no code
Identifying Fairness Issues in Automatically Generated Testing Content
Natural language generation tools are powerful and effective for generating content.
Machine Learning Techniques with Fairness for Prediction of Completion of Drug and Alcohol Rehabilitation
Demographic data is highly categorical which led to binary encoding being used and various fairness measures being utilized to mitigate bias of nine demographic variables.
Sum of Group Error Differences: A Critical Examination of Bias Evaluation in Biometric Verification and a Dual-Metric Measure
Biometric Verification (BV) systems often exhibit accuracy disparities across different demographic groups, leading to biases in BV applications.
Unveiling and Mitigating Generalized Biases of DNNs through the Intrinsic Dimensions of Perceptual Manifolds
Building fair deep neural networks (DNNs) is a crucial step towards achieving trustworthy artificial intelligence.
Fair Concurrent Training of Multiple Models in Federated Learning
We show how our fairness-based learning and incentive mechanisms impact training convergence and finally evaluate our algorithm with multiple sets of learning tasks on real world datasets.
Fairness Incentives in Response to Unfair Dynamic Pricing
We find that, upon deploying a learned tax and redistribution policy, social welfare improves on that of the fairness-agnostic baseline, and approaches that of the analytically optimal fairness-aware baseline for the multi-armed and contextual bandit settings, and surpassing it by 13. 19% in the full RL setting.
Bt-GAN: Generating Fair Synthetic Healthdata via Bias-transforming Generative Adversarial Networks
In conclusion, our research introduces a novel and professional approach to addressing the limitations of synthetic data generation in the healthcare domain.
Enforcing Conditional Independence for Fair Representation Learning and Causal Image Generation
We are able to enforce conditional independence of the diffusion autoencoder latent representation with respect to any protected attribute under the equalized odds constraint and show that this approach enables causal image generation with controllable latent spaces.
Security and Privacy Product Inclusion
In this paper, we explore the challenges of ensuring security and privacy for users from diverse demographic backgrounds.
Privacy-Preserving Debiasing using Data Augmentation and Machine Unlearning
Data augmentation is widely used to mitigate data bias in the training dataset.
