Scholarly publication record and open access repository entry.
Proceedings of the ACM Conference on Fairness, Accountability, and Transparency (ACM FAccT) Vol. 8(1), pp. 610-623(2025). DOI: 10.1145/3442188.3445922
Examines the environmental costs, training data curation risks, potential for algorithmic bias amplification, and limitations in statistical language models lacking grounded semantic understanding.
Foundational literature examining environmental, societal, and algorithmic limitations of large language models.
Open Access article distributed under the terms of the Creative Commons Attribution 4.0 International License (CC BY 4.0).
Official publication DOI link: https://doi.org/10.1145/3442188.3445922