Health, science, education and everyday life.
Research reviewed 13 September 2026Examples of useful AI already studied or made available
AI-supported radiologists, compared with standard double reading.
MASAI randomized trial, about 106,000 women; 2026 update. These results do not establish lower mortality.
Similar or better reliability for extreme river floods in watersheds without local gauges.
Nature study, 2024. Forecast accuracy, not a measured number of lives saved; warnings still need to reach people.
Predicted protein structures made freely available through AlphaFold’s database.
2022 database expansion, documented by EMBL-EBI in 2023. Predictions help researchers investigate biology; they are not experimentally confirmed structures or approved treatments.
Higher combined English, AI-knowledge and digital-skills scores after six weeks.
World Bank randomized trial. Standard deviations measure effect size; this is not a 31% gain. Result is for the full programme, including teachers.
Change relative to respective control outcomes
A tutor with teacher-designed safeguards largely removed the harm, without establishing a positive unaided-exam effect.
people were offline in 2025.
Online 74% Offline 26%
ITU estimate; one person = 1% of world population. Internet access is not AI access or AI literacy.
Share of respondents · 2025 report
48,340 respondents across 47 countries.
University of Melbourne / KPMG survey, Nov 2024–Jan 2025. Self-reports; samples are not a census of humanity and some overrepresent university education.
Separate Ada Lovelace / Alan Turing survey: 3,513 people. A public preference, not evidence a particular law works.
A Nature study found discriminatory judgments associated with African American English in tested models.
Controlled prompts and models, not a measured real-world hiring-discrimination rate.
Belief in a chosen conspiracy theory after personalized AI conversations.
Science study; effects persisted at two months. Belief ratings among participants, not a 20% reduction in society’s misinformation.
Verify claims against original sources. In education, measure learning without AI as well as assisted performance.
Test across affected groups, limit sensitive data, document failures and give people access to human review and an appeal.
Assess affordability, language and accessibility. For infrastructure, publish local resource data and give communities a role in decisions.
Primary university studies, government and intergovernmental reports, ethical-AI research and company disclosures published from September 2021–September 2026. This is a curated guide, not an exhaustive systematic review. Older underlying data are labelled.
Estimates, forecasts, surveys and observed results are kept distinct. Company claims are identified as disclosures. Charts are redrawn from cited data, with rounded values and calculated shares labelled; no missing years are interpolated.
Industry comparisons show scope and date because electricity, onsite water and supply-chain footprints are different measures. Findings do not automatically generalize beyond the studied people, products or settings.
Each “Notes” button gives the finding, scope and limits.
Download source ledger ↗International Energy Agency
International Energy Agency
Lawrence Berkeley National Laboratory
Google researchers
Luccioni, Viguier & Ligozat
Wang et al. · Nature Computational Science
International Labour Organization
World Economic Forum
Stanford Digital Economy Lab
Brynjolfsson, Li & Raymond · Quarterly Journal of Economics
Stanford HAI
Otis et al. · Management Science / Berkeley Haas
International Telecommunication Union
Lund University · The Lancet
Hofmann et al. · Nature
Costello, Pennycook & Rand · Science / MIT Sloan
National Institute of Standards and Technology
Google · Michael Terrell
Microsoft · Brad Smith & Melanie Nakagawa
Microsoft · Steve Solomon
Microsoft · Scott Guthrie
Amazon · Tyler Greenawalt
McDonald et al. · MIT Lincoln Laboratory and collaborators
MIT Han Lab · Yang et al.
Luccioni, Jernite & Strubell · FAccT
Xu et al. · Princeton / Joule
Science Based Targets initiative
Science Based Targets initiative
Harvard Business School · Rachel Layne / McFowland et al.
Ada Lovelace Institute & Alan Turing Institute
University of Melbourne & KPMG · Gillespie, Lockey and colleagues
Andrew A. Chien · Stanford Energy Seminar
MIT Energy Initiative · Emma Strubell and symposium participants
AI Now Institute
International Energy Agency
U.S. Energy Information Administration
U.S. Geological Survey
European Environment Agency
United Nations Statistics Division
International Energy Agency
Sony Interactive Entertainment
Ofcom · commissioned research by Carnstone
LinkedIn Economic Graph
Cui, Demirer, Jaffe, Musolff, Peng & Salz · Microsoft Research
Penn Wharton Budget Model · University of Pennsylvania
Nearing et al. · Google Research and collaborators · Nature
EMBL European Bioinformatics Institute
International Energy Agency
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