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Workers demand human oversight as AI reshapes workplaces in USA

A large majority of US workers support stronger AI workplace protections and see labour unions as the most trusted defenders of employee rights, according to an AFL-CIO poll. The findings highlight growing concern over how AI is being used in employment decisions and workplace management.

The survey of 1,588 respondents found over 90% support for human oversight in employment decisions, alongside strong backing for transparency, accountability and AI safeguards. A significant share also supported expanding unionisation to help workers negotiate protections related to automation.

Respondents expressed high levels of concern over undisclosed AI monitoring in the workplace, with most saying employers fail to clearly explain when or how AI tools are being used. Many workers said they view labour unions as more trustworthy than employers, political parties or tech companies in managing AI’s impact on jobs.

Union representatives said AI is increasingly used in scheduling, performance tracking and healthcare decisions, often without adequate consultation. The poll suggests broad demand for enforceable rules ensuring AI does not replace human judgement or reduce job security without worker consent.

Why does it matter?

The findings point to a broader structural tension between rapid AI adoption in workplaces and the slower development of governance frameworks that protect labour rights.

As AI becomes embedded in hiring, monitoring and decision-making systems, questions over accountability, transparency and human oversight are shifting from technical issues to core employment rights.

The strong preference for union-led safeguards also signals a potential rebalancing of power in the digital economy, where workers increasingly seek collective mechanisms to influence how automation is deployed and to ensure it does not erode job security or professional autonomy.

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US EDA launches AI workforce training programme

The US Economic Development Administration has announced approximately $25 million in funding for a new AI Upskill Accelerator Pilot Program to support AI workforce training.

The programme will fund industry-driven partnerships that design and implement AI training models for workers and businesses in sectors considered important to regional economies. EDA says the initiative is intended to support workforce development approaches that can scale, adapt and become self-sustaining as AI technologies continue to evolve.

The funding opportunity links the programme to the Trump administration’s 2025 Artificial Intelligence Action Plan, which includes goals to accelerate AI development, support adoption across industries and strengthen US leadership in the technology. EDA says the programme is part of efforts to empower American workers to use AI tools and support industries tied to regional growth.

Deputy Assistant Secretary and Chief Operating Officer Ben Page said AI is becoming ‘a core driver of productivity and growth across industries’ and that workers need AI skills so regions can attract investment, adopt advanced technologies and sustain long-term economic growth.

The pilot will support workforce development in an emerging technology area while helping businesses and workers build the skills needed to use AI in the workplace. Applications for the programme are open until 10 July 2026.

Why does it matter?

The programme shows how AI policy is increasingly being linked to regional economic development and workforce readiness, not only research or infrastructure. By funding industry-driven training models, the EDA is trying to prepare workers and local economies for AI adoption while helping businesses close skills gaps that could affect productivity, investment and competitiveness.

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WEF report says HR leaders will shape the success of AI transformation

AI is reshaping how companies organise labour, distribute decision-making and redesign internal operations, making workforce strategy a central part of AI adoption.

Writing for the World Economic Forum, Al-Futtaim Group HR director David Henderson argues that many AI projects fail because organisations focus too heavily on technology while neglecting the need to change work, accountability, and operational processes.

The article says successful AI adoption depends on how effectively businesses combine human judgement with machine-driven systems, rather than treating automation as a standalone software rollout.

Using Garry Kasparov’s ‘advanced chess’ model after his 1997 defeat to IBM’s Deep Blue as an example, Henderson highlights how humans working alongside computers eventually outperformed both machines and grandmasters operating independently.

He suggests the same principle is now emerging across modern enterprises, where stronger results come from integrating AI directly into operational workflows rather than isolating it in technical departments.

The article identifies four major responsibilities for HR leaders during AI transformation. As ‘design architects’, Chief Human Resources Officers are expected to redefine which decisions remain human-led, which become AI-assisted and how accountability is distributed across organisations. As ‘capability stewards’, they must build continuous AI learning systems rather than rely on occasional employee training programmes.

HR leaders are also described as ‘adoption catalysts’, responsible for helping frontline employees integrate AI into daily workflows, and as ‘transition guardians’, tasked with managing concerns linked to surveillance, bias, fairness, employability and workforce trust.

Several companies are cited as examples of that transition. Procter & Gamble embedded AI engineers and data scientists directly within operational business units rather than centralising them within analytics teams.

Zurich Insurance developed enterprise-wide AI learning systems focused on transferable skills and workforce redeployment, while Al-Futtaim enabled frontline retail teams to develop AI-supported customer recommendation systems through agile operational groups rather than top-down executive planning.

Why does it matter?

AI competitiveness increasingly depends on organisational adaptability instead of access to technology alone. Workforce redesign, reskilling systems, internal trust, and operational flexibility are becoming critical strategic advantages as automation expands across industries. WEF’s argument highlights how HR departments are evolving from administrative functions into central actors shaping AI governance, labour transformation, and long-term business resilience.

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AI productivity claims need stronger scrutiny according to Ada Lovelace Institute’s findings

The Ada Lovelace Institute has warned that AI productivity claims in the UK public sector need stronger scrutiny, as headline estimates are already shaping spending, workforce planning and public service reform.

In a policy briefing on AI and public services, the institute says UK government communications, industry reports and third-party analyses frequently present AI as a tool for cutting costs, saving time and boosting growth. It argues that stronger evidence is needed to assess whether those claims translate into public value.

The briefing notes that the UK’s 2025 Spending Review committed to ‘a step change in investment in digital and AI across public services’, informed by estimates of potential savings and productivity benefits that run as high as £45 billion per year.

Many current estimates rely on limited or uncertain evidence, the institute argues. Studies often measure first-order effects, such as time savings or cost reductions, while paying less attention to outcomes that matter for public services, including service quality, equity, citizen experience, institutional capacity and worker well-being.

The briefing also warns that productivity claims often fail to fully account for implementation costs, trade-offs, transition periods and the opportunity cost of prioritising AI investment over other public spending.

Several methodological concerns are identified in AI productivity research, including reliance on task automation models, self-reported surveys and limited triangulation across methods. The institute also highlights the growing use of large language models to assess which tasks they can perform, warning that this creates a circular dynamic in which AI systems are used to judge their own capabilities.

Headline figures can obscure mixed evidence, with productivity estimates varying widely and positive findings often receiving more attention than contradictory or null results. Industry involvement can also shape what gets researched and how results are framed, particularly when AI companies fund studies, provide tools or publish their own reports.

To improve the evidence base, the Ada Lovelace Institute calls for productivity research to reflect uncertainty, report ranges rather than single headline numbers and measure outcomes that matter for public services. It recommends more independent research, transparent methodologies, longer-term studies and measurement built into AI deployments from the start, including tracking service quality, error rates, staff well-being and citizen satisfaction.

Why does it matter?

Public-sector AI is increasingly being justified through promises of efficiency, savings and productivity growth. If those claims are based on weak or narrow evidence, governments risk making major investment and workforce decisions before understanding the real costs, trade-offs and effects on service quality.

The briefing is important because it shifts the question from whether AI can save time in isolated tasks to whether AI improves public services in practice. That includes outcomes such as fairness, reliability, staff well-being, citizen experience and institutional capacity, which are harder to measure than headline savings but central to public value.

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Australia launches national AI platform ‘AI.gov.au’

The Department of Industry, Science and Resources has announced the launch of AI.gov.au through the National Artificial Intelligence Centre. The platform is designed to help organisations adopt AI safely and responsibly in line with the National AI Plan.

AI.gov.au provides a central source of guidance, tools and resources to support businesses and not-for-profits. It aims to help users identify AI opportunities, plan implementation, manage risks and build internal capability.

The platform’s development was informed by research and engagement with industry and government, highlighting the need for clear starting points, practical advice and support for AI organisational change. It also supports the AI Safety Institute’s work by improving access to safety guidance.

Initial features focus on small and medium-sized enterprises and include training, case studies and adoption tools, with further updates planned. The initiative reflects efforts to strengthen AI uptake and governance in Australia.

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California expands digital democracy platform for AI policy debate

California’s Governor is expanding Engaged California, a digital democracy initiative designed to give residents a direct voice in shaping AI policy across the state. The programme invites Californians to share how AI is affecting their jobs, industries, and communities, with the findings expected to help guide future state policy decisions.

The initiative will begin with a public participation phase, during which residents can submit experiences and recommendations through the state’s online platform. A second phase, later in 2026, will bring together a smaller representative group of residents for live deliberative forums focused on AI’s economic and social impact. The process aims to identify areas of public consensus on how government should respond to rapidly evolving AI technologies.

State officials described ‘Engaged California’ as a first-in-the-nation deliberative democracy programme inspired partly by Taiwan’s digital governance model. Instead of functioning like a social media platform or public poll, the initiative is designed to encourage structured discussion and collaborative policymaking around emerging technologies.

California also used the announcement to highlight broader AI initiatives already underway, including AI procurement reforms, workforce training partnerships with major technology companies, AI-powered wildfire detection systems, cybersecurity assessments, and responsible governance frameworks.

Officials said the state aims to balance innovation with safeguards related to child safety, deepfakes, digital likeness protections, and AI accountability.

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European Commission updates guidance on generative AI use in research

The European Commission has updated the ERA Living Guidelines on the responsible use of generative AI in research, reflecting the growing use of AI tools across scientific work. The revised guidance aims to support researchers, research organisations and funding bodies in adopting generative AI while maintaining core principles of research integrity.

The guidelines emphasise reliability, honesty, respect and accountability, including transparency over AI use, protection of privacy and confidential information, and responsibility for research outputs. They also stress that researchers remain ultimately responsible for scientific output and should verify AI-generated results.

New recommendations address risks linked to the use of generative AI by third parties, including in meetings, note-taking, summaries and document overviews, where confidential information, data protection or intellectual property rights may be affected. The guidelines encourage researchers and organisations to inform third parties about the use of such tools and related risks.

A specific addition concerns the risk of ‘hidden prompts’, where instructions may be secretly embedded in documents or inputs to influence generative AI tools. The guidelines call on research funding organisations to remain aware of such risks, set rules prohibiting manipulation where relevant, and introduce appropriate safeguards in IT systems used to process information.

Developed through the European Research Area Forum, the guidelines are intended as a non-binding supporting tool for the research community. The Commission says they will be updated regularly and that users can continue to provide feedback as generative AI and the surrounding policy landscape evolve.

Why does it matter?

Generative AI is becoming part of everyday research workflows, from drafting and summarising to proposal preparation and document analysis. The updated guidelines show that research integrity risks now extend beyond individual misuse to organisational processes, third-party tools and hidden technical behaviours that may affect scientific judgement. Shared guidance across the European Research Area can help institutions adopt AI without weakening transparency, accountability or trust in research.

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Automation fuels inequality more than productivity gains, study finds

A new study co-authored by economists from Massachusetts Institute of Technology and Yale University finds that automation in the United States has often been driven less by productivity gains and more by firms’ efforts to reduce labour costs.

Rather than replacing workers to maximise efficiency, companies have frequently targeted employees earning a ‘wage premium’, effectively lowering higher-than-average salaries within comparable roles.

The research suggests this pattern has contributed significantly to widening income inequality while delivering only limited productivity improvements.

The analysis, which examines data spanning multiple decades and industries, indicates that automation has disproportionately affected higher-earning workers within affected groups. It also estimates that inefficient automation deployment may have offset a large share of potential productivity gains over time.

Researchers argue that the findings highlight a structural tension in how automation is applied, where short-term cost reduction can take priority over long-term economic efficiency, shaping both wage distribution and overall growth dynamics in the US economy since 1980.

Why does it matter?

The findings challenge the assumption that automation primarily improves efficiency and productivity, showing instead that firms can strategically use it to reshape wage structures and concentrate economic gains.

From a broader perspective, this helps explain why technological progress has not translated evenly into higher productivity or shared prosperity, while also highlighting how corporate incentives can steer innovation in ways that deepen inequality across labour markets.

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Generative AI guidance issued by Australia’s New South Wales tribunal

The New South Wales Civil and Administrative Tribunal has issued guidance on the acceptable use of generative AI in tribunal proceedings as part of Privacy Awareness Week NSW 2026, which this year focuses on personal information risks in the age of AI.

According to NCAT, generative AI tools may be used to assist with administrative and organisational tasks such as summarising material, organising information, or preparing chronologies. At the same time, the tribunal warns that such tools can create privacy risks if users enter personal, sensitive, or confidential information.

The guidance is set out in NCAT Procedural Direction 7 on the use of generative AI, together with an accompanying fact sheet. NCAT says the aim is to clarify when generative AI may be used in tribunal-related work while reinforcing obligations to protect personal and confidential information.

The tribunal also draws a clear line around evidentiary material. Generative AI must not be used to generate or alter evidence in tribunal proceedings, including statements, affidavits, statutory declarations, character references, or other evidentiary documents.

NCAT further states that generative AI must not be used to generate content for an expert report unless the tribunal has given permission. It is encouraging parties and their representatives to review the guidance before using such tools in proceedings.

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ILO warns lifelong learning is critical for the future AI economy

The International Labour Organization has warned t hat governments must place lifelong learning at the centre of economic and social policy as AI, digitalisation and demographic shifts continue transforming labour markets worldwide. The organisation said stronger and more inclusive learning systems are necessary to prevent widening inequality between workers, industries and countries.

According to the ILO’s new report, titled ‘Lifelong learning and skills for the future’, only 16% of people aged between 15 and 64 participated in structured training during the previous year. Access remains significantly higher among full-time employees in formal companies, where employer-supported training reaches 51%.

The ILO report warns that workers in informal jobs and smaller enterprises continue relying mainly on learning through experience instead of structured education programmes. Furthermore, the study found that employers increasingly seek combinations of digital, socio-emotional, communication and problem-solving skills rather than narrow technical expertise alone.

While demand for AI-related capabilities is expected to increase, the report noted that most workers currently use ready-made AI tools that require broader digital literacy, critical thinking and collaborative abilities instead of specialist engineering knowledge.

The ILO also highlighted the growing importance of green and care economy skills. It estimates that 32% of workers globally already perform environmentally relevant tasks, while demand for long-term care workers could almost double by 2050.

The organisation called for greater public investment, stronger institutional coordination and inclusive lifelong learning strategies capable of supporting workers throughout rapidly changing technological and economic transitions.

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