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...

HMRC trial of child benefit crackdown wrongly suspected fraud in 46% of cases

Exclusive: Almost half of families flagged as emigrants based on Home Office travel data were still living in UK

theguardian.com

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.

The Actuary's Final Word on Algorithmic Decision Making

Paul Meehl's foundational work "Clinical versus Statistical Prediction," provided early theoretical justification and empirical evidence of the superiority of statistical methods over clinical judgmen...

arxiv.org

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

Understanding Fixed Predictions via Confined Regions

Machine learning models can assign fixed predictions that preclude individuals from changing their outcome. Existing approaches to audit fixed predictions do so on a pointwise basis, which requires ac...

arxiv.org

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

Explanations are a means to an end

Modern methods for explainable machine learning are designed to describe how models map inputs to outputs--without deep consideration of how these explanations will be used in practice. This paper arg...

arxiv.org

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... 👇

Julian Skirzynski@jskirzynski.bsky.social · last yr.

Right to explanation laws assume explanations help people detect algorithmic discrimination. But is there any evidence for that? In our latest work w/ David Danks @berkustun, we show explanations fail to help people, even under optimal conditions. PDF shorturl.at/yaRua

“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

Harry Cheon@scheon.com · last yr.

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

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.

Ben Recht@beenwrekt.bsky.social · last yr.

I have a theory. arxiv.org/abs/2501.03457

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

Learning under Temporal Label Noise

Many time series classification tasks, where labels vary over time, are affected by label noise that also varies over time. Such noise can cause label quality to improve, worsen, or periodically chang...

arxiv.org

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

Home | HCXAI

ACM CHI 2025 Workshop on Human-Centered Explainable AI (HCXAI). May 2025 (Yokohama, Japan & hybrid). Submit your Paper (EasyChair)

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...

AI and the Risk of Consumer Harm

People often talk about “safety” when discussing the risks of AI causing harm. AI safety means different things to different people, and those looking for a definition here will be disappointed.

ftc.gov

📣 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...

Denied by AI: How Medicare Advantage plans use algorithms to cut off care for seniors in need

A STAT investigation found artificial intelligence is driving Medicare Advantage denials to new heights, cutting off care for seniors.

statnews.com

The New York Times@nytimes.com · 2y ago

UnitedHealthcare has been accused of using algorithms to deny treatments and refusing coverage of nursing care to stroke patients. UnitedHealthcare and its parent company now face a class-action lawsuit over its use of the algorithm.

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...

Saloni@scientificdiscovery.dev · 2y ago

Since I've been here for a while… here are some useful features on Bluesky that you might not have discovered yet! 🧵