37 citations · 106 across the 23 of their papers we have counts for
13 papers · 1 filter
Accounting for Stochasticity in Studies of Large Language Model Refusal
Emma Lurie, Stephanie T. Wang, Sorelle A. Friedler +1
We present preliminary empirical evidence that single-observation queries are insufficient for evaluations of LLM refusal behaviors. Using a longitudinal auditing system, we issued…
AI in Search Reduces Publisher Referrals Without Improving User Experience: Experimental Evidence
Stephanie T. Wang, Jeffrey Gleason, Yakov Bart +2
The integration of generative AI into web search delivers synthesized answers to user queries, changing how people navigate and assess information, while raising concerns about the…
What Current AI Benchmarks Leave Unmeasured: Modality, Search, Citations, and Implications (for Safety Evaluations)
Ro Encarnación, Tina Behzad, Emma Lurie +1
Large language model (LLM) benchmark evaluations are routinely used to support claims about model safety, reliability, and deployment readiness. Yet most evaluations rely on a sing…
The Algorithmic Flattening of Sound: Computational Evidence and Justice Implications of AI Music Homogenization
Zoe Slendebroek, Danaé Metaxa
This paper audits whether large-scale generative music systems exhibit measurable musical homogenization relative to human-produced music, and develops a justice-centered account o…
The Beginning of ChatGPT Ads
Emma Lurie, Ro Encarnación, Sorelle A. Friedler +1
This paper presents the first empirical study of advertising content being rolled out in the user-facing online interfaces of large language models (LLMs). We systematically examin…
MonitrLLM: A Community-Centered Evaluation Infrastructure for Large Language Models
Victor Ojewale, Ro Encarnación, Suresh Venkatasubramanian +1
Benchmark suites assess model capability on controlled tasks; large-scale conversation corpora capture naturalistic use without user feedback; and in-interface feedback mechanisms…
Triangulating Across U.S. Federal AI Transparency Regimes
Emma Lurie, Emma Fauser, Qing He +2
Federal AI systems can deny benefits or flag individuals for deportation, but the public disclosures meant to make those systems visible are fragmented and unevenly detailed. This…
Do Language Models Pass the Bechdel Test? Auditing Gender Biases in LLM-Generated Screenplays
Megha N. Govindu, Stephanie T. Wang, Sorelle A. Friedler +1
As large language models (LLMs) are increasingly used in media production from journalistm to filmmaking, what impact do they have on the stories being told? Prior work has shown L…
Cost-of-Ethics Crisis: Beliefs, Decisions, and Justifications in the Job Searches of Computer Science Students in Canada and the United States
Mohamed Abdalla, Sahar Abdalla, Alicia Cappello +4
Workplace norms in computer science have received growing attention due to a series of recent ethical scandals. One response has been a push to improve the ethics education provide…
Understanding teens' self-beliefs when learning to construct and deconstruct AI/ML systems: Developing a survey instrument
Luis Morales-Navarro, Deborah Fields, Michael T. Giang +3
Despite growing calls to foster AI literacy, there are few available survey instruments designed for children and youth that study computational empowerment alongside construction…
Building to Understand: Examining Teens' Technical and Socio-Ethical Pieces of Understandings in the Construction of Small Generative Language Models
Luis Morales-Navarro, Daniel J. Noh, Lucianne Servat +3
The rising adoption of generative AI/ML technologies increases the need to support teens in developing AI/ML literacies. Child-computer interaction research argues that constructio…
"You Can Actually Do Something'': Shifts in High School Computer Science Teachers' Conceptions of AI/ML Systems and Algorithmic Justice
Daniel J. Noh, Deborah A. Fields, Yasmin B. Kafai +1
The recent proliferation of artificial intelligence and machine learning (AI/ML) systems highlights the need for all people to develop effective competencies to interact with and e…
Rapid Testing, Duck Lips, and Tilted Cameras: Youth Everyday Algorithm Auditing Practices with Generative AI Filters
Lauren Vogelstein, Vedya Konda, Deborah Fields +3
Today's youth have extensive experience interacting with artificial intelligence and machine learning applications on popular social media platforms, putting youth in a unique posi…