Research using a global energy-economy model indicates that AI-driven productivity gains increase net annual CO2 emissions by 0.47–1.8 gigatonnes under parallel adoption. Enabled emissions from fossil sectors consistently exceed avoided emissions from renewables. Achieving net reductions requires renewable productivity gains to be four to five times greater than fossil gains.

This report distils lessons from 25 organisations on leveraging technology to strengthen human rights due diligence in value chains. It categorises various tools, provides case studies, and outlines practical steps for responsible AI adoption, emphasising that technology must complement, rather than replace, human judgement and meaningful stakeholder engagement.

Curating 56 successful fraud protection solutions, this report details strategies across intelligence sharing, telecom security, and biometrics. It highlights how AI-driven oversight and multi-sector collaboration, such as Malaysia's NSRC and UK's reimbursement rules, can reduce losses and restore consumer trust in digital financial ecosystems.

This International Labour Organization report examines the psychosocial risks of AI systems in workplaces. It identifies how algorithmic management and advanced robotics impact mental health, autonomy, and privacy. The paper advocates for integrated regulatory frameworks across labour law and safety standards to protect workers from emerging digital hazards.

This research examines AI diffusion through US firms, distinguishing between organisational adoption and worker-level use. While adoption is growing, it remains concentrated in large, knowledge-intensive sectors. Findings indicate AI primarily augments worker tasks, with capital substitution being more prevalent than labour displacement at this stage of technological integration.

This research examines how automating technology affects white-collar career dynamics and human capital. It identifies a potential 'human-capital trap' where automation reduces entry-level learning. The paper recommends targeted taxes on automation and subsidies for task-frontier expansion to maintain long-term welfare and professional skill development.

This research series provides a comprehensive annual analysis of digital news consumption globally. It tracks audience trends across 48 markets, focusing on trust, platform usage, and the transition to digital formats. This benchmark series informs stakeholders about the evolving information environment and changing news habits across diverse demographics.

This report examines how artificial intelligence is transforming capital markets by shifting the industry from informational scarcity to analytical abundance. It introduces a structural framework involving capability, adoption, substitution, and recomposition, exploring alternative future states and the evolving nature of fiduciary accountability, market stability, and professional expertise.

This research provides international data on firm-level artificial intelligence adoption across the US, UK, Germany, and Australia. While current impacts on employment and productivity are small, senior executives predict a 1.4 per cent productivity boost and a 0.7 per cent reduction in employment over the next three years.

Research demonstrates that Large Language Models exhibit purpose-conditioned bias when informed of downstream tasks. Goal-aware prompting leads to in-sample overfitting and inflated performance before knowledge cutoffs. Results indicate that disclosing objectives compromises neutrality, necessitating the separation of measurement and evaluation in AI-assisted workflows to ensure statistical validity.

This report examines the intersection of artificial intelligence and sustainability transitions, detailing opportunities and risks across environment, energy, labour, finance, and democracy. It proposes a three-layered governance framework and a phased roadmap for global scientific alignment, interim safety measures, and a binding international framework convention to ensure societal well-being.

This research investigates the demand for explainable AI in high-stakes credit settings. It finds that decision-makers strategically avoid explanations that reveal racial or gender bias to preserve moral wiggle room. Additionally, behavioural biases cause individuals to undervalue explanations even when they complement private information and improve decision accuracy.

This research examines how technological standardisation commoditises labour by making workers interchangeable. It develops a model showing that while this process increases productivity, it simultaneously reduces worker bargaining power and wages. This framework explains the declining large-firm wage premium and the divergence between productivity and pay in services.

This report examines the hidden human costs of content moderation and data labelling in Colombia, Kenya, and the Philippines. It details systemic physical, psychological, and economic harms caused by lead firm power, informal employment, and intense target pressure, while identifying significant barriers to corporate accountability and worker rights.

This IPPR paper examines how social media platforms have shifted from user-led spaces to algorithm-driven, influencer-dominated environments through 'sticky gatekeeping'. A UK survey found only 18 per cent of feed posts were personal content. The report recommends regulatory reform, algorithmic transparency, and development of a public social media platform.

This Chatham House paper examines four trends — rising defence and dual-use investment, the growth of 'patriotic tech', the push for sovereign AI, and concerns over an AI valuation bubble — that could multipolarise the global AI race, and offers recommendations for private sector preparedness.