Impact on bias mitigation algorithms to variations in inferred sensitive attribute uncertainty
Concerns about the trustworthiness, fairness, and privacy of AI systems are growing, and strategies for mitigating these concerns are still in their infancy. One approach to improve trustworthiness and fairness in AI systems is to use bias mitigation algorithms. However, most bias mitigation algorit...
Saved in:
| Main Authors: | Yanchen Wang, Lisa Singh |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2025-03-01
|
| Series: | Frontiers in Artificial Intelligence |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/frai.2025.1520330/full |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Attention-guided convolutional network for bias-mitigated and interpretable oral lesion classification
by: Adeetya Patel, et al.
Published: (2024-12-01) -
Demographic bias mitigation at test-time using uncertainty estimation and human–machine partnership
by: Anoop Krishnan Upendran Nair, et al.
Published: (2025-03-01) -
Metrics and Algorithms for Identifying and Mitigating Bias in AI Design: A Counterfactual Fairness Approach
by: Dongsoo Moon, et al.
Published: (2025-01-01) -
Granger causal inference for climate change attribution
by: Mark D Risser, et al.
Published: (2025-01-01) -
Enhancing Fairness in Credit Assessment: Mitigation Strategies and Implementation
by: Melvin Kisten, et al.
Published: (2024-01-01)