@inproceedings{10.1145/3715070.3748285,
author = {Scheuerman, Morgan Klaus and Zhao, Dora and Andrews, Jerone T. A. and Birhane, Abeba and Liao, Q. Vera and Panagiotidou, Georgia and Chitre, Pooja and Pine, Kathleen and Walker, Shawn and Zhao, Jieyu and Xiang, Alice},
title = {Responsibly Training Foundation Models: Actualizing Ethical Principles for Curating Large-Scale Training Datasets in the Era of Massive AI Models},
year = {2025},
isbn = {9798400714801},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3715070.3748285},
doi = {10.1145/3715070.3748285},
abstract = {AI technologies have become ubiquitous, influencing domains from healthcare to finance and permeating our daily lives. Concerns about the values underlying the creation and use of datasets to develop AI technologies are growing. Current dataset practices often disregard critical ethical issues, despite the fact that data represents and impacts real people. While progress has been made in establishing best practices for curating smaller datasets in a more ethical fashion, the unprecedented scale of training data in the era foundation models presents unique hurdles for which AI researchers and practitioners must now face. This workshop aims to unite interdisciplinary researchers and practitioners in an effort to identify the challenges unique to curating datasets for large-scale foundation models—and then begin to ideate best practices for tackling those challenges. Drawing from CSCW’s tradition of interdisciplinary exchange, our aim is to cultivate a diverse community of researchers and practitioners interested in defining the future of ethical responsibility in the composition, process, and release of large-scale datasets for foundation model training. We will disseminate the outcomes of this workshop to the HCI community and beyond by developing a conceptual framework of both the challenges and potential solutions associated specifically with curating datasets for foundation models.},
booktitle = {Companion Publication of the 2025 Conference on Computer-Supported Cooperative Work and Social Computing},
pages = {99–105},
numpages = {7},
keywords = {Fairness, ethics, responsible AI, foundation models, generative AI, datasets, machine learning, responsible artificial intelligence, human-centric artificial intelligence, algorithmic bias, values in design, work practice},
location = {
},
series = {CSCW Companion '25}
}