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Author

D. Metaxa

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Review Aug 2026

AI in Search Reduces Publisher Referrals Without Improving User Experience: Experimental Evidence

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 downstream impacts on publishers who supply the underlying content. We conduct a preregistered field experiment (N=1,100) on Google Search,...

Stephanie T. Wang, Jeffrey Gleason, Y. Bart et al. · 0 citations
#large language models Aug 2026

The Beginning of ChatGPT Ads

The first empirical study of advertising content being rolled out in the user-facing online interfaces of large language models (LLMs) finds that accounts begin receiving ads 14 days after account creation, and that lower-income accounts are more likely to receive ads.

Emma Lurie, Ro Encarnación, Sorelle A. Friedler et al. · 0 citations
#human-computer interacti... Preprint Sep 2026

Accounting for Stochasticity in Studies of Large Language Model Refusal

Preliminary empirical evidence that single-observation queries are insufficient for evaluations of LLM refusal behaviors is presented, with reliable quantification of refusals required between 15 and 25 repeated queries, well above the single-observation standard common in existing evaluations.

Emma Lurie, Stephanie T. Wang, Sorelle A. Friedler et al. · 0 citations
#generative ai Aug 2026

The Algorithmic Flattening of Sound: Computational Evidence and Justice Implications of AI Music Homogenization

It is argued that these patterns matter not as an aesthetic curiosity but as a justice-relevant condition, shaping which musical styles become legible, valued, and economically rewarded as generated outputs increasingly circulate at scale.

Zoe F. Slendebroek, D. Metaxa · 0 citations
#large language models Aug 2026

MonitrLLM: A Community-Centered Evaluation Infrastructure for Large Language Models

MonitrLLM is introduced, open-source infrastructure for community-centered LLM evaluations that links full conversation transcripts to user-reported task intent and outcome assessments, treating all three as primary evaluative signals rather than optional metadata.

Victor Ojewale, Ro Encarnación, S. Venkatasubramanian et al. · 0 citations
#human-computer interacti... Aug 2026

What Current AI Benchmarks Leave Unmeasured: Modality, Search, Citations, and Implications (for Safety Evaluations)

It is argued that AI safety evaluations should systematically account for modality, multi-run consistency, search conditions, and response-level behaviors to better reflect how deployed AI systems behave in practice.

Ro Encarnación, Tina Behzad, Emma Lurie et al. · 0 citations

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