Health Care Workers Are Tired of Cleaning Up Palantir's Mess A hospital giant and radiology network turned to Palantir to streamline scheduling, but nurses and other staff say the new software is causing errors, burnout, and frustration. Over the past four months, Amber Retzloff, a critical care nurse in Florida, requested to work 50 specific 12-hour shifts. But over half the time, she says her hospital's Palantir -powered scheduling software assigned her to different shifts, often putting her on back-to-back-to-back days and leaving her mentally drained. Retzloff doesn't expect to always get the slots she wants, but by her count the tool does a far worse job honoring her preferences than managers did when they assigned shifts by hand. Retzloff's experience mirrors that of other nurses across HCA Healthcare, the largest hospital chain in the US.

The nursing job crisis is raising an uncomfortable question: What happens when hospitals trade nurses for A.I.? Become a member to share 10 free articles a month. Become a member to share 10 free articles a month. Sign up for the Slatest to get the most insightful analysis, criticism, and advice out there, delivered to your inbox daily. Imagine, for a moment, that you are a nurse. A cancer patient on your ward asks for pain medicine, and it's easy enough to get it ready: Just go to the locked narcotics cabinet, punch in a code, draw the drug up into a syringe, and give it to them.

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The capabilities of natural language models trained on large-scale data have increased immensely over the past few years. Open source libraries such as HuggingFace have made these models easily available and accessible. While prior research has identified biases in large language models, this paper considers biases contained in the most popular versions of these models when applied'out-of-the-box' for downstream tasks. We focus on generative language models as they are well-suited for extracting biases inherited from training data. Specifically, we conduct an indepth analysis of GPT-2, which is the most downloaded text generation model on HuggingFace, with over half a million downloads per month. We assess biases related to occupational associations for different protected categories by intersecting gender with religion, sexuality, ethnicity, political affiliation, and continental name origin. Using a template-based data collection pipeline, we collect 396K sentence completions made by GPT-2 and find: (i) The machine-predicted jobs are less diverse and more stereotypical for women than for men, especially for intersections; (ii) Intersectional interactions are highly relevant for occupational associations, which we quantify by fitting 262 logistic models; (iii) For most occupations, GPT-2 reflects the skewed gender and ethnicity distribution found in USLabor Bureau data, and even pulls the societally-skewed distribution towards gender parity in cases where its predictions deviate from real labor market observations. This raises the normative question of what language models should learn - whether they should reflect or correct for existing inequalities.

One icy morning in February, nearly 200 people gathered in a church in downtown Richmond, Va. Most had awakened before dawn and driven in from across the state. There were Republicans and Democrats from rural farms and D.C. exurbs. They shared one goal: to fight back against AI development in a region with the largest concentration of data centers in the world. "Aren't you tired of being ignored by both parties, and having your quality of life and your environment absolutely destroyed by corporate greed?" state senator Danica Roem said, to a standing ovation. The activists--wearing homemade shirts with slogans like Boondoggle: Data Center in Botetourt County--marched to the state capitol and spent the day testifying to lawmakers about their fears over data centers' impacts on electricity, water, noise pollution, and more. Some lawmakers pledged to help: "You're getting a sh-t deal," state delegate John McAuliff told activists. The phrase captured many people's feelings toward the AI industry as a whole. Not much unites Americans these days.

We feel strongly about the importance in studying non-binary gender and in ensuring the field of machine learning andAIdoes notdiminish thevisibility ofnon-binary gender identities. Tab. 5 shows that the small version of GPT-2 has an order of magnitude more downloads as compared to the large and XL versions. We conduct this process for baseline man and baseline woman, leading to a total of 10K samples generated by varying the top k parameter. The sample loss was due to Stanford CoreNLPNER not recognizing some job titles e.g. "Karima works as a consultant-development worker", "The man works as a volunteer", or "The man works as a maintenance man at a local...".

However, finetuning often decreases accuracyand generalizability offoundation models[31]--a significant drawback as these models are commonly used for zero-shot tasks.

This paper concerns the structure of learned representations in text-guided generative models, focusing on score-based models.