298 citations · 438 across the 9 of their papers we have counts for
9 papers
NLP Systems That Can't Tell Use from Mention Censor Counterspeech, but Teaching the Distinction Helps
Kristina Gligoric, Myra Cheng, Lucia Zheng +2
The use of words to convey speaker's intent is traditionally distinguished from the `mention' of words for quoting what someone said, or pointing out properties of a word. Here we…
AnthroScore: A Computational Linguistic Measure of Anthropomorphism
Myra Cheng, Kristina Gligoric, Tiziano Piccardi +1
Anthropomorphism, or the attribution of human-like characteristics to non-human entities, has shaped conversations about the impacts and possibilities of technology. We present Ant…
CoMPosT: Characterizing and Evaluating Caricature in LLM Simulations
Myra Cheng, Tiziano Piccardi, Diyi Yang
Recent work has aimed to capture nuances of human behavior by using LLMs to simulate responses from particular demographics in settings like social science experiments and public o…
The Surveillance AI Pipeline
Pratyusha Ria Kalluri, William Agnew, Myra Cheng +3
A rapidly growing number of voices argue that AI research, and computer vision in particular, is powering mass surveillance. Yet the direct path from computer vision research to su…
Marked Personas: Using Natural Language Prompts to Measure Stereotypes in Language Models
Myra Cheng, Esin Durmus, Dan Jurafsky
To recognize and mitigate harms from large language models (LLMs), we need to understand the prevalence and nuances of stereotypes in LLM outputs. Toward this end, we present Marke…
Easily Accessible Text-to-Image Generation Amplifies Demographic Stereotypes at Large Scale
Federico Bianchi, Pratyusha Kalluri, Esin Durmus +7
Machine learning models that convert user-written text descriptions into images are now widely available online and used by millions of users to generate millions of images a day.…
Ethical and social risks of harm from Language Models
Laura Weidinger, John Mellor, Maribeth Rauh +20
This paper aims to help structure the risk landscape associated with large-scale Language Models (LMs). In order to foster advances in responsible innovation, an in-depth understan…
Social Norm Bias: Residual Harms of Fairness-Aware Algorithms
Myra Cheng, Maria De-Arteaga, Lester Mackey +1
Many modern machine learning algorithms mitigate bias by enforcing fairness constraints across coarsely-defined groups related to a sensitive attribute like gender or race. However…
Human Preference-Based Learning for High-dimensional Optimization of Exoskeleton Walking Gaits
Maegan Tucker, Myra Cheng, Ellen Novoseller +4
Optimizing lower-body exoskeleton walking gaits for user comfort requires understanding users' preferences over a high-dimensional gait parameter space. However, existing preferenc…