Laissez-Faire Harms: Algorithmic Biases in Generative Language Models

arXiv:2404.07475v1 Announce Type: cross
Abstract: The rapid deployment of generative language models (LMs) has raised concerns about social biases affecting the well-being of diverse consumers. The extant literature on generative LMs has primarily examined bias via explicit identity prompting. However, prior research on bias in earlier language-based technology platforms, including search engines, has shown that discrimination can occur even when identity terms are not specified explicitly. Studies of bias in LM responses to open-ended prompts (where identity classifications are left …

abstract arxiv bias biases concerns consumers cs.ai cs.cl cs.cy cs.lg deployment diverse generative however identity language language models literature lms platforms prior prompting research search social technology type via