Nari Johnson

@narijohnson.bsky.social

researching AI’s impacts on society 🔍

Most Multilingual benchmarks measure what models know, not what they can reliably do. Our #ACL2026 paper introduces functional benchmarks in six languages from English to Yoruba to test whether models can actually execute tasks across languages, not just answer fixed questions about them. 🧵1/n

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Attending the @eval-eval.bsky.social workshop at #ACL2026 to present this paper! Happy to chat about all things LLM-as-a-judge + the role of human feedback/participation in designing evals ✨

Nari Johnson@narijohnson.bsky.social · last mo.

All AI evaluations embed assumptions about what "good" behavior looks like. Our #FAccT2026 paper explores how we can center the perspectives of impacted communities - specifically, subjects of AI-generated media - in designing LLM-as-a-judge evaluation rubrics.

A flyer teasing our research paper. The flyer contains a visualization of our approach: First, community members participate in creating an evaluation rubric, visualized as a list of criteria. The rubric is then given as input to an LLM judge.

Super excited to share a new #FAccT2026 paper with Nel Escher and Nikola Banovic! We show that algorithm auditing policies in the US are wildly insufficient, as they don't account for how public sector algorithms actually function in practice. dl.acm.org/doi/abs/10.1...

Algorithm Auditing Policies Rest on Flawed Assumptions About Public Sector Systems | Proceedings of the 2026 ACM Conference on Fairness, Accountability, and Transparency

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dl.acm.org

RLHF is, instead, a form of survey, which is sensitive to the framing of the solicitation questions and other survey design practices. It's also a form of inverted content moderation - sometimes described as "content moderation via vibes". This moderation operates over generation, not screening.

We want human preferences to be an objective measure of human values, but this view is not grounded in RLHF practice. Instead, the raters who provide preferences are screened to express certain values and are then trained to provide preferences that adhere to some specific criteria. #FAccT2026

All AI evaluations embed assumptions about what "good" behavior looks like. Our #FAccT2026 paper explores how we can center the perspectives of impacted communities - specifically, subjects of AI-generated media - in designing LLM-as-a-judge evaluation rubrics.

A flyer teasing our research paper. The flyer contains a visualization of our approach: First, community members participate in creating an evaluation rubric, visualized as a list of criteria. The rubric is then given as input to an LLM judge.

🔬Science can play a critical role in guiding policy interventions. How can we make our research legible to the policymakers who work on AI? Our tutorial "From FAccT to Policy Impact" explores how we can translate our research in a specific format: the policy brief! #FAccT2026

A text flyer advertising our policy tutorial, "From FAccT to Policy Impact"

🚨The 2025 AI Agent Index is out! 🚨 Amidst recent buzz over 🦀 and NIST's new agent initiative, we find: - Selective reporting – esp. on safety - Almost all agents backend just 3 model families - Many agents don’t ID themselves as bots online - Big US/China gaps - And more…

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PhD admissions visits/open houses are starting to happen, and I got a comment on an old Reddit post where I was offering advice, and realized that it's actually really good advice. So here it is! (And this applies whether you've already been admitted to the program or not.) 🧵

A new report by the Center for Tech Responsibility at Brown University and the ACLU uses computational tools to analyze legislative trends on AI across 1,804 state and federal bills, while offering recommendations for how to integrate the technology into policy analysis.

Making Sense of AI Policy Using Computational Tools | TechPolicy.Press

A new report examines how to use computational tools to evaluate policy, with AI policy as a case study.

techpolicy.press

US CAISI is hiring -- the internal govt name for the role is "IT Specialist" but it is effectively a research scientist role! Salary is $120,579 to - $195,200 per year, and you get to work on AI evaluation within government agencies! Job posting (**closes EOD 12/28/2025**): lnkd.in/exJgkqr5

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Did you know that one base model is responsible for 94% of model-tagged NSFW AI videos on CivitAI? This new paper studies how a small number of models power the non-consensual AI video deepfake ecosystem and why their developers could have predicted and mitigated this.

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