I'll be @neuripsconf.bsky.social presenting Strategic Hypothesis Testing (spotlight!) tldr: Many high-stakes decisions (e.g., drug approval) rely on p-values, but people submitting evidence respond strategically even w/o p-hacking. Can we characterize this behavior & how policy shapes it? 1/n

Spread the word! 📢 The FATE (Fairness, Accountability, Transparency, and Ethics) group at @msftresearch.bsky.social in NYC is hiring interns and postdocs to start in summer 2026! 🎉 Apply by *December 15* for full consideration.

UK government project using AI to find benefit fraud resulted in: - A 46% false fraud rate - Anguish for families who were wrongly accused of fraud and had benefits stopped - Months of additional work for government, setting up a hotline, correcting false fraud www.theguardian.com/society/2025...

I’m giving an IDE seminar at @mitsloan.bsky.social tomorrow at 11am, on optimizing AI as decision support. Joint work w/ @ziyang.bsky.social @yifanwu.bsky.social @jasonhartline.bsky.social @berkustun.bsky.social Come by if you’re around! www.eventbrite.com/e/fall-2025-...

Who teaches an undergraduate principles of programming languages class? Looking for some inspiration to teach one at UCSD

In a new paper, I try to resolve the counterintuitive evidence of Meehl’s “clinical vs statistical prediction” problems: Statistics only wins because the game is rigged.

Machine learning models can assign fixed predictions that preclude individuals from changing their outcome. Think credit applicants that can never get a loan approved, or young patients that can never get an organ transplant - no matter how sick they are!

Excited to be chatting about our new paper "Understanding Fixed Predictions via Confined Regions" (joint work with @berkustun.bsky.social, Lily Weng, and Madeleine Udell) at #ICML2025! 🕐 Wed 16 Jul 4:30 p.m. PDT — 7 p.m. PDT 📍East Exhibition Hall A-B #E-1104 🔗 arxiv.org/abs/2502.16380

Paper: www.arxiv.org/abs/2506.22740 Blog post: statmodeling.stat.columbia.edu/2025/07/02/w...

ExplainableAI has long frustrated me by lacking a clear theory of what an explanation should do. Improve use of a model for what? How? Given a task what's max effect explanation could have? It's complicated bc most methods are functions of features & prediction but not true state being predicted 1/

Having a lot of FOMO not being able to be in person at #FAccT2025 but enjoying the virtual transmission 💻. Tomorrow Jakob will be presenting our paper "Perils of Label Indeterminacy: A Case Study on Prediction of Neurological Recovery After Cardiac Arrest".

Explanations don't help us detect algorithmic discrimination. Even when users are trained. Even when we control their beliefs. Even under ideal conditions... 👇

“Science is a smart, low cost investment. The costs of not investing in it are higher than the risk of doing so… talk to people about science.” - @kevinochsner.bsky.social makes his case to the field #sans2025

Hey AI folks - stop using SHAP! It won't help you debug [1], won't catch discrimination [2], and makes no sense for feature importance [3]. Plus - as we show - it also won't give recourse. In a paper at #ICLR we introduce feature responsiveness scores... 1/ arxiv.org/pdf/2410.22598

When RAG systems hallucinate, is the LLM misusing available information or is the retrieved context insufficient? In our #ICLR2025 paper, we introduce "sufficient context" to disentangle these failure modes. Work w Jianyi Zhang, Chun-Sung Ferng, Da-Cheng Juan, Ankur Taly, @cyroid.bsky.social

Denied a loan, an interview, or an insurance claim by machine learning models? You may be entitled to a list of reasons. In our latest w @anniewernerfelt.bsky.social @berkustun.bsky.social @friedler.net, we show how existing explanation frameworks fail and present an alternative for recourse

Absolute banger.

Many ML models predict labels that don’t reflect what we care about, e.g.: – Diagnoses from unreliable tests – Outcomes from noisy electronic health records In a new paper w/@berkustun, we study how this subjects individuals to a lottery of mistakes. Paper: bit.ly/3Y673uZ 🧵👇

🚨 Excited to announce a new paper accepted at #ICLR2025 in Singapore! “Learning Under Temporal Label Noise” We tackle a new challenge in time series ML: label noise that changes over time 🧵👇 arxiv.org/abs/2402.04398

The CHI Human-Centered Explainable AI Workshop is back! Paper submissions: Feb 20 hcxai.jimdosite.com

🧵on the CFPB and less discriminatory algorithms. last week, in its supervisory highlights, the Bureau offered a range of impressive new details on how financial institutions should be searching for less discriminatory algorithms.

Engaging discussions on the future of #AI in #healthcare at this week's ICHPS, hosted by @amstatnews.bsky.social. JCHI's @kdpsingh.bsky.social shared insights on the safety & equity of #MachineLearning algorithms and examined bias in large language models.

Safety that matters www.ftc.gov/policy/advoc...

📣 CANAIRI: the Collaboration for Translational AI Trials! Co lead @xiaoliu.bsky.social @naturemedicine.bsky.social Perhaps most important to AI translation is the local silent trial. Ethically, and from an evidentiary perspective, this is essential! url.au.m.mimecastprotect.com/s/pQSsClx14m...

🪩New paper🪩 (WIP) appearing at @neuripsconf.bsky.social Regulatable ML and Algorithmic Fairness AFME workshop (oral spotlight). In collaboration with @s010n.bsky.social and Manish Raghavan, we explore strategies and fundamental limits in searching for less discriminatory algorithms.

I'm seeking a postdoc to work with me and @kenholstein.bsky.social on evaluating AI/ML decision support for human experts: statmodeling.stat.columbia.edu/2024/12/10/p... P.S. I'll be at NeurIPS Thurs-Mon. Happy to talk about this position or related mutual interests! Please repost 🙏

There is just about a month left before the abstract deadline for @facct.bsky.social 2025🥳😳🙈. Really looking forward to seeing all the submissions. If you are submitting, don’t forget to check out the Author Guide (and the reviewer and AC guide as well). facctconference.org/2025/aguide

Starter pack #ML for Healthcare go.bsky.app/PJKJ8vK by ‪@berkustun.bsky.social‬

My group is hiring postdoc(s) and I will be at #NeurIPS2024 if you want to talk - send me a DM or find me on Whova! UK-based, 2 year position. We work on AI/ML for health, specifically building and *understanding/explaining* deep (multimodal) models on healthcare data, especially medical imaging.

“Denied by AI,” the multi-part STAT News investigation of how #UnitedHealthcare used an opaque algorithmic system to deny care to people who needed it is a #mustread www.statnews.com/2023/03/13/m...

Personally I think they should have a superset of: (1) empower their students (2) foment collaborations (3) help produce clarity (4) intellectually move to their students not other way around (5) teach core skills (6) mentor effectively (7) produce a sense of safety and joy

This is a great thread that lists many of the features: bsky.app/profile/scie...

Introducing scE2G: a new model to link enhancers to target genes using single-cell data. Excited that scE2G will enable building enhancer maps in hundreds of cell types in the human body! Wonderful collaboration with @randersson.bsky.social @613weilin.bsky.social @mayayayas.bsky.social Thread👇

I tried to find everyone who works in the area but I certainly missed some folks so please lmk... go.bsky.app/BYkRryU

Couldn't find a machine learning for health starter pack so I made one.  DM/Reply if you want to be added! go.bsky.app/PJKJ8vK

I heard bluesky likes links. So here is a link to a book I’m writing. github.com/NannyML/The-...

Ok #FAccT folks - I'm still looking around for people. Who am I missing? go.bsky.app/EQF5Ne1

Couldn't find a machine learning for health starter pack so I made one.  DM/Reply if you want to be added! go.bsky.app/PJKJ8vK

There’s a long line of literature starting from the ICP paper arxiv.org/abs/1501.01332, with IRM being an important (if imperfect) checkpoint arxiv.org/abs/1907.02893 I also really like the anchor regression paper arxiv.org/abs/1801.06229 and this is our work (calibrate!) arxiv.org/abs/2102.10395

We (me & @berkustun.bsky.social) created a starter pack of researchers working on algorithmic decision-making & fairness. Other platforms still seem to dominate for many ML-related topics, but maybe this will help Probably missed people, so let us know if you have suggestions! go.bsky.app/851zVkg

Please share! We're hiring postdoctoral researchers to work on responsible machine learning at UCSD! Topics include fairness, explainability, robustness, and safety. For more, see berkustun.com/postdoc/