Things to Do in L.A. Tap to enable a layout that focuses on the article. Labor unions urge Gov. Gavin Newsom, California lawmakers to rein in artificial intelligence Lorena Gonzalez, with the California Labor Federation, supports legislation to protect workers from AI. This is read by an automated voice. Please report any issues or inconsistencies here . Labor unions urge Gov. Gavin Newsom to protect workers from AI-driven job losses and workplace surveillance.

Blizzard's quality assurance workers finally have a union contract The agreement includes guardrails around AI in the workplace and guaranteed pay increases. Almost three years after starting the bargaining process with Microsoft, quality assurance workers at two Blizzard locations have ratified a union contract . The agreement covers 60 workers at Blizzard Albany and Blizzard Austin. The agreement includes guaranteed pay increases across the three years of the contract, assurances that workers will be given fair credits and recognition on games that ship, discrimination-free disability accommodations, restrictions on crunch (i.e. Stronger rules around the use of AI are included in the contract as well.

A logo of Ubisoft is seen at its booth during the Gamescom video games trade fair at the Trade Fair Center in Cologne, western Germany. When deciding which video game to buy, Is it fun? is no longer the only consideration. Given the state of the industry, Do I want to support this company? is arguably more important. Take, for example, Ubisoft, where things seem to unravel more each day. After the floundering publisher floated even more layoffs this week, workers at its Paris headquarters said, Enough is enough.

A labour rights organisation claims it has found evidence of worker exploitation in a Chinese factory that makes the viral Labubu dolls. China Labor Watch (CLW), a US-based non-governmental organisation, alleges that its investigation found that one of Pop Mart's suppliers made employees work excessive overtime shifts, sign blank or incomplete contracts and did not give them paid leave. The furry Labubu dolls have surged in popularity around the world in recent years and are best known for selling toys in blind boxes, which hide its content from buyers until it is opened. Pop Mart told the BBC that it is investigating the claims. The Beijing-based toy retailer said it appreciated the details from the review and that it will firmly require companies making its toys to correct their practices if the allegations are found to be true.

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Naturally, we can achieve impact parity through purposeful treatment disparity. One line of papers aims to reconcile the two parities proposing disparate learning processes (DLPs). Here, the sensitive feature is used during training but a group-blind classifier is produced.

Autonomous systems that make substantive decisions about people must conform to relevant anti-discrimination legislation.

Large language models (LLMs) are increasingly deployed across high-impact domains, from clinical decision support and legal analysis to hiring and education, making fairness and bias evaluation before deployment critical. However, existing evaluations lack grounding in real-world scenarios and do not account for differences in harm severity, e.g., a biased decision in surgery should not be weighed the same as a stylistic bias in text summarization. To address this gap, we introduce HALF (Harm-Aware LLM Fairness), a deployment-aligned framework that assesses model bias in realistic applications and weighs the outcomes by harm severity. HALF organizes nine application domains into three tiers (Severe, Moderate, Mild) using a five-stage pipeline. Our evaluation results across eight LLMs show that (1) LLMs are not consistently fair across domains, (2) model size or performance do not guarantee fairness, and (3) reasoning models perform better in medical decision support but worse in education. We conclude that HALF exposes a clear gap between previous benchmarking success and deployment readiness.

Data from online job postings are difficult to access and are not built in a standard or transparent manner. Data included in the standard taxonomy and occupational information database (O*NET) are updated infrequently and based on small survey samples. We adopt O*NET as a framework for building natural language processing tools that extract structured information from job postings. We publish the Job Ad Analysis Toolkit (JAAT), a collection of open-source tools built for this purpose, and demonstrate its reliability and accuracy in out-of-sample and LLM-as-a-Judge testing. We extract more than 10 billion data points from more than 155 million online job ads provided by the National Labor Exchange (NLx) Research Hub, including O*NET tasks, occupation codes, tools, and technologies, as well as wages, skills, industry, and more features. We describe the construction of a dataset of occupation, state, and industry level features aggregated by monthly active jobs from 2015 - 2025. We illustrate the potential for research and future uses in education and workforce development.