SOTAVerified

Fairness

Papers

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Showing 1426–1450 of 5676 papers

TitleStatusHype
Detecting algorithmic bias in medical-AI models using trees—0
Detecting Shortcut Learning for Fair Medical AI using Shortcut Testing—0
Image Counterfactual Sensitivity Analysis for Detecting Unintended Bias—0
Detecting Prefix Bias in LLM-based Reward Models—0
Detecting Unforeseen Data Properties with Diffusion Autoencoder Embeddings using Spine MRI data—0
Detection and Mitigation of Bias in Ted Talk Ratings—0
Detection of Groups with Biased Representation in Ranking—0
Developing A Fair Individualized Polysocial Risk Score (iPsRS) for Identifying Increased Social Risk of Hospitalizations in Patients with Type 2 Diabetes (T2D)—0
Developing and Deploying Industry Standards for Artificial Intelligence in Education (AIED): Challenges, Strategies, and Future Directions—0
BACSA: A Bias-Aware Client Selection Algorithm for Privacy-Preserving Federated Learning in Wireless Healthcare Networks—0
Developing a Philosophical Framework for Fair Machine Learning: Lessons From The Case of Algorithmic Collusion—0
Don't Forget What I did?: Assessing Client Contributions in Federated Learning—0
DIF: A Framework for Benchmarking and Verifying Implicit Bias in LLMs—0
DifFaiRec: Generative Fair Recommender with Conditional Diffusion Model—0
Different Horses for Different Courses: Comparing Bias Mitigation Algorithms in ML—0
Differentiable Optimal Adversaries for Learning Fair Representations—0
Differential Adjusted Parity for Learning Fair Representations—0
Differentially Private and Fair Classification via Calibrated Functional Mechanism—0
Differentially Private and Fair Deep Learning: A Lagrangian Dual Approach—0
Differentially Private Data Release on Graphs: Inefficiencies and Unfairness—0
Differentially Empirical Risk Minimization under the Fairness Lens—0
Coordinated Multi-Armed Bandits for Improved Spatial Reuse in Wi-Fi—0
Differentially Private Empirical Risk Minimization under the Fairness Lens—0
Differentially Private Fair Learning—0
Cooperative IoT Data Sharing with Heterogeneity of Participants Based on Electricity Retail—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
11D-CSNNPredictive Equality (age)99.86—Unverified
21D-CSNNPredictive Equality (age)97.8—Unverified
#ModelMetricClaimedVerifiedStatus
11D-CSNNPredictive Equality (age)96.87—Unverified
#ModelMetricClaimedVerifiedStatus
11D-CSNNPredictive Equality (age)98.97—Unverified
#ModelMetricClaimedVerifiedStatus
11D-CSNNPredictive Equality (age)98.45—Unverified
#ModelMetricClaimedVerifiedStatus
11D-CSNNPredictive Equality (age)98.68—Unverified
#ModelMetricClaimedVerifiedStatus
11D-CSNNPredictive Equality (age)99.31—Unverified
#ModelMetricClaimedVerifiedStatus
1Neighbour LearningDegree of Bias (DoB)0.49—Unverified
#ModelMetricClaimedVerifiedStatus
1Neighbour LearningDegree of Bias (DoB)6.26—Unverified
#ModelMetricClaimedVerifiedStatus
1Neighbour LearningDegree of Bias (DoB)1.96—Unverified