SOTAVerified

Fairness

Papers

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Showing 4326–4350 of 5676 papers

TitleStatusHype
A Pre-trained Framework for Multilingual Brain Decoding Using Non-invasive Recordings—0
Rate-Splitting Multiple Access: The First Prototype and Experimental Validation of its Superiority over SDMA and NOMA—0
A Quantitative Evaluation of the Expressivity of BMI, Pose and Gender in Body Embeddings for Recognition and Identification—0
A quest for a fair schedule: The Young Physicists' Tournament—0
A Ranking Approach to Fair Classification—0
A Rate-Splitting Strategy to Enable Joint Radar Sensing and Communication with Partial CSIT—0
Are Big Recommendation Models Fair to Cold Users?—0
An Equity-Aware Recommender System for Curating Art Exhibits Based on Locally-Constrained Graph Matching—0
Are Commercial Face Detection Models as Biased as Academic Models?—0
Are Conventional SNNs Really Efficient? A Perspective from Network Quantization—0
Are demographically invariant models and representations in medical imaging fair?—0
Are Elites Meritocratic and Efficiency-Seeking? Evidence from MBA Students—0
Are fairness metric scores enough to assess discrimination biases in machine learning?—0
Are Fairness Perceptions Shaped by Income Inequality? Evidence from Latin America—0
Are generative models fair? A study of racial bias in dermatological image generation—0
A Regret bound for Non-stationary Multi-Armed Bandits with Fairness Constraints—0
A Reinforcement Learning Approach for RIS-aided Fair Communications—0
A Reinforcement Learning Approach to Optimize Available Network Bandwidth Utilization—0
Reinforcement Learning for Joint Optimization of Multiple Rewards—0
Are Linear Regression Models White Box and Interpretable?—0
Are Models Trained on Indian Legal Data Fair?—0
Are My Deep Learning Systems Fair? An Empirical Study of Fixed-Seed Training—0
Are Pretrained Multilingual Models Equally Fair Across Languages?—0
A Possibility in Algorithmic Fairness: Can Calibration and Equal Error Rates Be Reconciled?—0
A Resource-Adaptive Approach for Federated Learning under Resource-Constrained Environments—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