Sacha Alanoca

Thanks @WomeninAIEthics for including me in the 2022 list! bit.ly/32igxZQ In these uncertain times, happy to be in such good company among inspiring women, friends & colleagues. Thank you @MiaD & team for your leadership to create so much #solidarity in #AI & beyond💓

Interested in rethinking power, ethics, & governance in the AI age? Want to be part of an interdisciplinary community of critical thinkers on campus? Join the Stanford Critical AI Working Group's kick-off for our official launch this Friday, March 6, 12-1:15PM!

“The US isn’t deregulating AI. It’s regulating where most people aren’t looking.” Beneath the free-market rhetoric, the US is actively involved in the building blocks of AI systems: chips and hardware. 🗞️ Our take w/ @Maroussia_____ in @guardian shorturl.at/OKT62

Sacha Alanoca & I dispel the deregulation narrative. theguardian.com/commentisfree/…

Many thanks to @BKCHarvard for their early support & to all the experts who generously shared insights on local legal contexts 🙏 Full paper here ▶️ arxiv.org/pdf/2505.13673

Next steps? 🔉Presenting at #FAccT25 & #IAPP25 this summer 🗺️ Expanding our geographical scope to include new laws like South Korea’s Basic AI Act 🧭Turning our paper & data viz into a dynamic tool for global AI cooperation Want to collaborate? Feel free to DM!

Key finding 4️⃣ Civic engagement remains a blind spot in #AIregulation. There's little data on whether public consultations translate into meaningful legal outcomes—or are merely performative. Are we engaging citizens or just checking boxes? 👀

🌎 What is the global landscape of #AI (de)regulation? 🌎 In our new #FAccT paper, we aim to cut through the hype & analyze emerging AI regulations across 5 early movers: the EU, US, China, Canada & Brazil. Cc @thesezickbeats @kevin_klyman @BKCHarvard Shira Gur-Arieh🧵

Key finding 3️⃣ Not all #AIregulation is equal. Hybrid frameworks—combining ex ante & ex post interventions with technology- & application-based rules—are the most comprehensive. They address societal harms & national security risks before AND after deployment.

Key finding 2️⃣ Innovation vs. regulation is a false dichotomy. China shows it’s possible to enforce mandatory AI safeguards while developing frontier models like #DeepSeek While regulatory goals differ across regions, it’s clear countries can lead the AI race & regulate it.

Key finding 1️⃣ We must clarify what #AIregulation means. The term is used ambiguously to describe both binding laws AND voluntary industry guidelines. Lines are strategically blurred between hard law & soft law, opening doors to regulatory capture. 🚩AI regulation ≠ AI policy

To democratize our insights, we collaborated with @HarvardGSD designers to translate dense AI legislation into accessible outputs. Our aim was to ensure all stakeholders—especially from civil society—are well-equipped to navigate this quickly evolving landscape.

We built a taxonomy comparing AI reg in 🇪🇺🇺🇸🇨🇳🇨🇦🇧🇷 along key metrics: 🔹Tech vs. application rules 🔹Ex ante vs. ex post interventions 🔹Horizontal vs. sectoral scope 🔹Centralized vs. decentralized enforcement 🔹Maturity of digital legal systems 🔹Stakeholder engagement

Pierre refused to accept unless it were also extended to Marie -- his wife, but also his coworker and fellow scientist. Together, they made fundamental advances. And so Marie Curie became the first female to break the Nobel Prize barrier... (2/n)

Marie Curie was the first woman to win the Nobel Prize. This didn't happen because she herself fought for it -- although she did fight hard the whole way to even get to that point. 👩‍🔬 What happened was that the award was offered to her husband, Pierre. (1/n) 🧵

NEW PAPER! 1. Small AI models often perform better than big in context. 2. Obsession w bigness has bad consequences, from climate, to power concentration, to surveillance, to research capture. This & what to do c/o @GaelVaroquaux, @SashaMTL and me ♥️👇 arxiv.org/abs/2409.14160

📢 AI Snake Oil is out today! I have too many feelings because this book has been five years in the making. In 2019 I gave a talk on AI Snake Oil and tweeted out the slides. I had no idea my career was about to change. Within a couple of days I had 30 or 40 invites to write a book or an article. I said no to all of them because I felt I needed to do more research to be able to write a book. So when @sayashk joined my team I knew this was the topic we were going to work on. The book is based on our research on limits to prediction, the dangers of predictive decision making, the reproducibility crisis in machine learning based science, the limits of large language models, and the risks of social media algorithms. The book is not an anti-technology screed. If our point was that all AI is useless, we wouldn’t need a book to say it. It’s precisely because of AI’s usefulness in many areas that overhyped claims and outright snake oil have been so successful in the market. It’s hard for people to tell these apart, and we hope our book can help. And while we think the tech industry should have much more accountability, the harms we describe are usually not solely due to tech, and much more often due to AI being an amplifier of existing problems in our society. A recurring pattern we point out in the book is that "broken AI is appealing to broken institutions" (Chapter 8). Since our book talks about what AI can’t do, AI boosters often tell us it’s going to be outdated soon. If you think so, I’d love to have a friendly bet! Read the book and tell us what you think will be wrong in two years or five years. (You can start with the intro chapter, which is available online; the link is in my pinned tweet.) The reason we’re confident of our assessments is that they are not based on limitations of the technology. Instead, they are based on inherent limits to prediction grounded in our sociological analysis. (The book owes a huge debt to Princeton sociologist @msalganik; our collaboration with him informed and inspired the book.) We also talk a lot about what we shouldn’t use AI for, even if we can. We’ve had many moments of hope and trepidation for the book as we worked on it over the years. We’re so grateful for the early interest the book has had, especially to the 40,000 people who subscribe to the AI Snake Oil newsletter, and the many podcasts that have had us on to talk about the book. We’ve had great reviews in The New Yorker, Publishers' Weekly (featured in the Top 10 science books for Fall 2024), and many other outlets. And we’ve had such a wonderful experience working with Princeton University Press. We’re also fortunate to have received incisive peer reviews from @MelMitchell1 , @mollycrockett , @chris_bail, and three anonymous reviewers. The book is so much better as a result. What's in the book Chapter 1: Introduction. We begin with a summary of our main arguments in the book. We discuss the definition of AI (and more importantly, why it is hard to come up with one), how AI is an umbrella term, what we mean by AI Snake Oil, and who the book is for. The entire introduction is available online. See the link in my pinned tweet. Chapter 2: How predictive AI goes wrong. Predictive AI is used to make predictions about people—will a defendant fail to show up for trial? Is a patient at high risk of negative health outcomes? Will a student drop out of college? These predictions are then used to make consequential decisions. Developers claim predictive AI is groundbreaking, but in reality it suffers from a number of shortcomings that are hard to fix. Chapter 3: Can AI predict the future? Are the shortcomings of predictive AI inherent, or can they be resolved? In this chapter, we look at why predicting the future is hard — with or without AI. While we have made consistent progress in some domains such as weather prediction, we argue that this progress cannot translate to other settings, such as individuals' life outcomes, the success of cultural products like books and movies, or pandemics. Chapter 4: The long road to generative AI. Recent advances in generative AI can seem sudden, but they build on a series of improvements over seven decades. In this chapter, we retrace the history of computing advances that led to generative AI. Chapter 5: Is advanced AI an existential threat? Claims about AI wiping out humanity are common. Here, we critically evaluate claims about AI's existential risk and find several shortcomings and fallacies in popular discussion of x-risk. We discuss approaches to defending against AI risks that improve societal resilience regardless of the threat of advanced AI. Chapter 6: Why can't AI fix social media? One area where AI is heavily used is content moderation on social media platforms. We discuss the current state of AI use on social media, and highlight seven reasons why improvements in AI alone are unlikely to solve platforms' content moderation woes. Chapter 7: Why do myths about AI persist? Companies, researchers, and journalists all contribute to AI hype. We discuss how myths about AI are created and how they persist. In the process, we hope to give you the tools to read AI news with the appropriate skepticism and identify attempts to sell you snake oil. Chapter 8: Where do we go from here? While the previous chapter focuses on the supply of snake oil, in the last chapter, we look at where the demand for AI snake oil comes from. We also look at the impact of AI on the future of work, the role and limitations of regulation, and conclude with vignettes of the many possible futures ahead of us. We have the agency to determine which path we end up on, and each of us can play a role. ------- We hope you find the book useful and look forward to hearing what you think.

The vast majority of research effort seems to be going into improving capability rather than reliability, and I think it should be the opposite.

In keeping with our core value of transparency @huggingface, @IreneSolaiman and @YJernite have put together details on almost everything we've formally written to policymakers (& their awesome staffs). 👇 huggingface.co/blog/policy-bl…

Join us with the AI & Tech Policy Caucus for "Surveillance Capitalism in the Age of AI" featuring Shoshana Zuboff (@shoshanazuboff): 🗓️ 4/11 ⏰ 1:00pm | Register: bit.ly/3QcHip1

"the Cloud now has a greater carbon footprint than the airline industry. A single data center can consume the equivalent electricity of 50,000 homes. At 200 terawatt hours (TWh) annually, data centers collectively devour more energy than some nation-states"thereader.mitpress.mit.edu/the-staggering…