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References (60)

[1]
Privacy and Fairness in Machine Learning: A Survey
2025Sina Shaham, Arash Hajisafi et al.
[2]
Feature Responsiveness Scores: Model-Agnostic Explanations for Recourse
2024Seung Hyun Cheon, Anneke Wernerfelt et al.
[3]
A Two-Stage Algorithm for Cost-Efficient Multi-instance Counterfactual Explanations
2024André Artelt, Andreas Gregoriades
[4]
A New Paradigm for Counterfactual Reasoning in Fairness and Recourse
2024Lucius E.J. Bynum, Joshua R. Loftus et al.
[5]
On the (In)Compatibility between Group Fairness and Individual Fairness
2024Shizhou Xu, Thomas Strohmer
[6]
Advancing Graph Counterfactual Fairness Through Fair Representation Learning
2024Zichong Wang, Zhibo Chu et al.
[7]
Fairness Aware Counterfactuals for Subgroups
2023Loukas Kavouras, Konstantinos Tsopelas et al.
[8]
GLOBE-CE: A Translation-Based Approach for Global Counterfactual Explanations
2023D. Ley, Saumitra Mishra et al.
[9]
PreCoF: counterfactual explanations for fairness
2023S. Goethals, David Martens et al.
[10]
DualFair: Fair Representation Learning at Both Group and Individual Levels via Contrastive Self-supervision
2023Sungwon Han, Seungeon Lee et al.
[11]
Auditing fairness under unawareness through counterfactual reasoning
2023Giandomenico Cornacchia, V. W. Anelli et al.
[12]
SAC-FACT: Soft Actor-Critic Reinforcement Learning for Counterfactual Explanations
2023Fatima Ezzeddine, Omran Ayoub et al.
[13]
Fairness and Explainability: Bridging the Gap Towards Fair Model Explanations
2022Yuying Zhao, Yu Wang et al.
[14]
"Explain it in the Same Way!" - Model-Agnostic Group Fairness of Counterfactual Explanations
2022André Artelt, Barbara Hammer
[15]
Fairness in Recommendation: Foundations, Methods, and Applications
2022Yunqi Li, H. Chen et al.
[16]
Fairness in recommender systems: research landscape and future directions
2022Yashar Deldjoo, D. Jannach et al.
[17]
Counterfactual explanations and how to find them: literature review and benchmarking
2022Riccardo Guidotti
[18]
Counterfactual Models for Fair and Adequate Explanations
2022Nicholas M. Asher, Lucas de Lara et al.
[19]
A Review on Fairness in Machine Learning
2022Dana Pessach, E. Shmueli
[20]
Learning Fair Node Representations with Graph Counterfactual Fairness
2022Jing Ma, Ruocheng Guo et al.

Showing 20 of 60 references

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"Develop a tool for generating fair counterfactual explanations to ensure unbiased decision-making in AI models."

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5

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2/4 signals

5

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