This work introduces Fairabel, a novel approach that repairs fairness by mutating the training data under the guidance of causal models and multi-objective optimization, and shows that Fairabel reduces ML software bias by 50% on average across three fairness metrics, outperforming the state-of-the-art with a 9% relativ...
Ying Xiao, Zhen-Peng Chen, Yepang Liu et al.· ACM Transactions on Software...· 0 citations
This workshop aims to define a roadmap for a world where AI Teammates and human developers build the future together, anchored by the launch of the AIDev dataset, which provides the empirical evidence needed to understand the behaviors of AI Teammates.
Hao Li, Haoxiang Zhang, Jie M. Zhang et al.· Proceedings of the 32nd ACM...· 2 citations
We are witnessing the emergence of Agentic Software Engineering (SE~3.0), where AI agents act as autonomous AI Teammates performing complex tasks such as coding, debugging, and testing. As AI Teammates generate a vast new category of digital artifacts, they introduce unique opportunities and challenges related to human...
Hao Li, Haoxiang Zhang, Jie M. Zhang et al.· Proceedings of the 32nd ACM...· 0 citations
Five widely used prompting strategies across five influential LLMs in the latest medical bias benchmark reveal substantial heterogeneity in both effectiveness and overhead across models, with no strategy proving universally effective and some even exacerbating bias.
Ying Xiao, Zhenpeng Chen, Jie M. Zhang· Philosophical transactions....· 1 citation
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