51 citations · 121 across the 32 of their papers we have counts for
6 papers · 1 filter
Understanding and Meeting Practitioner Needs When Measuring Representational Harms Caused by LLM-Based Systems
Emma Harvey, Emily Sheng, Su Lin Blodgett +4
The NLP research community has made publicly available numerous instruments for measuring representational harms caused by large language model (LLM)-based systems. These instrumen…
Rigor in AI: Doing Rigorous AI Work Requires a Broader, Responsible AI-Informed Conception of Rigor
Alexandra Olteanu, Su Lin Blodgett, Agathe Balayn +7
In AI research and practice, rigor remains largely understood in terms of methodological rigor -- such as whether mathematical, statistical, or computational methods are correctly…
AI Automatons: AI Systems Intended to Imitate Humans
Alexandra Olteanu, Solon Barocas, Su Lin Blodgett +3
There is a growing proliferation of AI systems designed to mimic people's behavior, work, abilities, likenesses, or humanness -- systems we dub AI automatons. Individuals, groups,…
A Taxonomy of Linguistic Expressions That Contribute To Anthropomorphism of Language Technologies
Alicia DeVrio, Myra Cheng, Lisa Egede +2
Recent attention to anthropomorphism -- the attribution of human-like qualities to non-human objects or entities -- of language technologies like LLMs has sparked renewed discussio…
Dehumanizing Machines: Mitigating Anthropomorphic Behaviors in Text Generation Systems
Myra Cheng, Su Lin Blodgett, Alicia DeVrio +2
As text generation systems' outputs are increasingly anthropomorphic -- perceived as human-like -- scholars have also increasingly raised concerns about how such outputs can lead t…
Position: Evaluating Generative AI Systems Is a Social Science Measurement Challenge
Hanna Wallach, Meera Desai, A. Feder Cooper +17
The measurement tasks involved in evaluating generative AI (GenAI) systems lack sufficient scientific rigor, leading to what has been described as "a tangle of sloppy tests [and] a…