And I'll be at @wimlworkshop.bsky.social and @neuripsconf.bsky.social Tue, Wed, and Thu next week if folks want to chat.

For all three positions, we welcome applicants with backgrounds in technical fields (e.g., ML, AI, NLP, statistics) and sociotechnical fields (e.g., human--computer interaction, information science, law, media studies, philosophy, science and technology studies, sociology).

And you can read more about STAC here: microsoft.com/en-us/resear...

You can read more about FATE here: microsoft.com/en-us/resear...

1) (Tomorrow!) Wed 7/16, 11am-1:30 pm PT poster for "Position: Evaluating Generative AI Systems Is a Social Science Measurement Challenge" (E. Exhibition Hall A-B, E-503)

Work led by @hannawallach.bsky.social + @azjacobs.bsky.social

arxiv.org/abs/2502.00561

Oh whoops! You are indeed correct -- it starts at 11am PT!

I also want to note that this paper has been in progress for many, many years, so we're super excited it's finally being published. It's also one of the most genuinely interdisciplinary projects I've ever worked on, which has made it particularly challenging and rewarding!!! ❤️

Check out the camera-ready version of our ACL Findings paper ("Taxonomizing Representational Harms using Speech Act Theory") to learn more!!! arxiv.org/pdf/2504.00928

Why does this matter? You can't mitigate what you can't measure, and our framework and taxonomy help researchers and practitioners design better ways to measure and mitigate representational harms caused by generative language systems.

Using this theoretical grounding, we provide new definitions for stereotyping, demeaning, and erasure, and break them down into a detailed taxonomy of system behaviors. By doing this, we unify many of the different ways representational harms have been previously defined.

We bring some much-needed clarity by turning to speech act theory—a theory of meaning from linguistics that allows us to distinguish between a system output’s purpose and its real-world impacts.

These are often called “representational harms,” and while they’re easy for people to recognize when they see them, definitions of these harms are commonly under-specified, leading to conceptual confusion. This makes them hard to measure and even harder to mitigate.

Check out the camera-ready version of our ICML position paper ("Position: Evaluating Generative AI Systems Is a Social Science Measurement Challenge") to learn more!!! arxiv.org/abs/2502.00561

(6/6)

Real talk: GenAI systems aren't toys. Bad evaluations don't just waste people's time---they can cause real-world harms. It's time to level up, ditch the apples-to-oranges comparisons, and start doing measurement like we mean it.

(5/6)

We propose a framework that cuts through the chaos: first, get crystal clear on what you're measuring and why (no more vague hand-waving); then, figure out how to measure it; and, throughout the process, interrogate validity like your reputation depends on it---because, honestly, it should.

(4/6)

Here's our hot take: evaluating GenAI systems isn't just some techie puzzle---it's a social science measurement challenge.

(3/6)

But there's a dirty little secret: the ways we evaluate GenAI systems are often sloppy, vague, and quite frankly... not up to the task.

(2/6)

Please spread the word to anyone who you think might be interested! We will begin reviewing applications on June 2.