Uncovering and Quantifying Social Biases in Code Generation
Yan Liu, Xiaokang Chen, et al.
NeurIPS 2023
The increasing integration of generative AI into work has amplified issues of disclosure, ownership, and accountability, including whether and how to acknowledge AI use, who owns AI-generated or co-created work, and who is accountable for risks. In response, governments, organizations, and researchers are introducing new policies, guidelines, and methods for enhanced transparency. However, the complex interplay between multiple stakeholders and technologies, coupled with growing AI agency, continues to spark debates about ownership and accountability of co-created work, leading to open questions about whether, when, and how to disclose and attribute human-AI co-created work. To address these emergent issues, this workshop aims to gather interdisciplinary researchers, practitioners, and experts to discuss key questions from law, technology, design, and HCI research standpoints, with the ultimate goal of promoting responsible generative AI use for work.
Yan Liu, Xiaokang Chen, et al.
NeurIPS 2023
Buse Korkmaz, Rahul Nair, et al.
AAAI 2025
Michael Muller, Vera Khovanskaya
CHIWORK 2025
Byungchul Tak, Shu Tao, et al.
IC2E 2016