12 citations · 29 across the 5 of their papers we have counts for
8 papers
A Keyword Based Approach to Understanding the Overpenalization of Marginalized Groups by English Marginal Abuse Models on Twitter
Kyra Yee, Alice Schoenauer Sebag, Olivia Redfield +3
Harmful content detection models tend to have higher false positive rates for content from marginalized groups. In the context of marginal abuse modeling on Twitter, such dispropor…
Societal Biases in Language Generation: Progress and Challenges
Emily Sheng, Kai-Wei Chang, Premkumar Natarajan +1
Technology for language generation has advanced rapidly, spurred by advancements in pre-training large models on massive amounts of data and the need for intelligent agents to comm…
Investigating Societal Biases in a Poetry Composition System
Emily Sheng, David Uthus
There is a growing collection of work analyzing and mitigating societal biases in language understanding, generation, and retrieval tasks, though examining biases in creative tasks…
"Nice Try, Kiddo": Investigating Ad Hominems in Dialogue Responses
Emily Sheng, Kai-Wei Chang, Premkumar Natarajan +1
Ad hominem attacks are those that target some feature of a person's character instead of the position the person is maintaining. These attacks are harmful because they propagate im…
Towards Controllable Biases in Language Generation
Emily Sheng, Kai-Wei Chang, Premkumar Natarajan +1
We present a general approach towards controllable societal biases in natural language generation (NLG). Building upon the idea of adversarial triggers, we develop a method to indu…
The Woman Worked as a Babysitter: On Biases in Language Generation
Emily Sheng, Kai-Wei Chang, Premkumar Natarajan +1
We present a systematic study of biases in natural language generation (NLG) by analyzing text generated from prompts that contain mentions of different demographic groups. In this…