4 papers
Algorithmic Fairness: Not a Purely Technical but Socio-Technical Property
Yijun Bian, Lei You, Yuya Sasaki +2
The rapid trend of deploying artificial intelligence (AI) and machine learning (ML) systems in socially consequential domains has raised growing concerns about their trustworthines…
FairSHAP: Preprocessing for Fairness Through Attribution-Based Data Augmentation
Lin Zhu, Yijun Bian, Lei You
Ensuring fairness in machine learning models is critical, particularly in high-stakes domains where biased decisions can lead to serious societal consequences. Existing preprocessi…
Towards Trustworthy Federated Learning
Alina Basharat, Yijun Bian, Ping Xu +1
This paper develops a comprehensive framework to address three critical trustworthy challenges in federated learning (FL): robustness against Byzantine attacks, fairness, and priva…
Joint Distribution-Informed Shapley Values for Sparse Counterfactual Explanations
Lei You, Yijun Bian, Lele Cao
Counterfactual explanations (CE) aim to reveal how small input changes flip a model's prediction, yet many methods modify more features than necessary, reducing clarity and actiona…