Angelina Wang

@angelinawang.bsky.social

Asst Prof at Cornell Info Sci and Cornell Tech. Responsible AI https://angelina-wang.github.io/

🥁🥁🥁 Newly out from us today in Science Advances: “Biased AI Writing Assistants Shift Users’ Attitudes on Societal Issues”. Large Language Models are providing users with autocomplete writing suggestions on many platforms. Could these suggestions shift users’ own attitudes? (spoiler: YES) (1/7)

Started a thread in the other place and bringing it over here - I really think we should be more vocal about the opportunities that lay at the intersection of these two options! So I'm starting a live thread of new roles as I become aware of them - feel free to add / extend / share :

Margaret Mitchell@mmitchell.bsky.social · 10mo ago

Life situations are bleak right now for a lot of people. In tech, the "Venn Diagram" of (1) positive work and (2) making enough money to support your family is increasingly non-overlapping. We all do what we can. This image has been living in my mind rent-free for months.

Tech Venn Diagram
Two circles, one says "Doing work that contributes beneficially to society."; the other says "Making enough income to support a family."

Can AI simulations of human research participants advance cognitive science? In @cp-trendscognsci.bsky.social, @lmesseri.bsky.social & I analyze this vision. We show how “AI Surrogates” entrench practices that limit the generalizability of cognitive science while aspiring to do the opposite. 1/

AI Surrogates and illusions of generalizability in cognitive science

Recent advances in artificial intelligence (AI) have generated enthusiasm for using AI simulations of human research participants to generate new know…

sciencedirect.com

Was beyond disappointed to see this in the AI Action Plan. Messing with the NIST RMF (which many private & public institutions currently rely on) feels like a cheap shot

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We have to talk about rigor in AI work and what it should entail. The reality is that impoverished notions of rigor do not only lead to some one-off undesirable outcomes but can have a deeply formative impact on the scientific integrity and quality of both AI research and practice 1/

Print screen of the first page of a paper pre-print titled "Rigor in AI: Doing Rigorous AI Work Requires a Broader, Responsible AI-Informed Conception of Rigor" by Olteanu et al.  Paper abstract: "In AI research and practice, rigor remains largely understood in terms of methodological rigor -- such as whether mathematical, statistical, or computational methods are correctly applied. We argue that this narrow conception of rigor has contributed to the concerns raised by the responsible AI community, including overblown claims about AI capabilities. Our position is that a broader conception of what rigorous AI research and practice should entail is needed. We believe such a conception -- in addition to a more expansive understanding of (1) methodological rigor -- should include aspects related to (2) what background knowledge informs what to work on (epistemic rigor); (3) how disciplinary, community, or personal norms, standards, or beliefs influence the work (normative rigor); (4) how clearly articulated the theoretical constructs under use are (conceptual rigor); (5) what is reported and how (reporting rigor); and (6) how well-supported the inferences from existing evidence are (interpretative rigor). In doing so, we also aim to provide useful language and a framework for much-needed dialogue about the AI community's work by researchers, policymakers, journalists, and other stakeholders."

Have you ever felt that AI fairness was too strict, enforcing fairness when it didn’t seem necessary? How about too narrow, missing a wide range of important harms? We argue that the way to address both of these critiques is to discriminate more 🧵

The US government recently flagged my scientific grant in its "woke DEI database". Many people have asked me what I will do. My answer today in Nature. We will not be cowed. We will keep using AI to build a fairer, healthier world. www.nature.com/articles/d41...

My ‘woke DEI’ grant has been flagged for scrutiny. Where do I go from here?

My work in making artificial intelligence fair has been noticed by US officials intent on ending ‘class warfare propaganda’.

nature.com

I am excited to announce that I will join the University of Zurich as an assistant professor in August this year! I am looking for PhD students and postdocs starting from the fall. My research interests include optimization, federated learning, machine learning, privacy, and unlearning.

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Our new piece in Nature Machine Intelligence: LLMs are replacing human participants, but can they simulate diverse respondents? Surveys use representative sampling for a reason, and our work shows how LLM training prevents accurate simulation of different human identities.

📢📢 Introducing the 1st workshop on Sociotechnical AI Governance at CHI’25 (STAIG@CHI'25)! Join us to build a community to tackle AI governance through a sociotechnical lens and drive actionable strategies. 🌐 Website: chi-staig.github.io 🗓️ Submit your work by: Feb 17, 2025

A poster advertising the first workshop on sociotechnical AI governance with a description of the workshop's core themes and faces of the organizers.

There are numerous leaderboards for AI capabilities and risks, for example fairness. In new work, we argue that leaderboards are misleading when the determination of concepts like “fairness” is always contextual. Instead, we should use benchmark suites.

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