[ { "id": "DIV-0005", "case_id": "AIBD-0105", "title": "Monetized climate value of GLAD alerts", "summary": "Researchers value avoided deforestation associated with African GLAD alert subscriptions at US$149–696 million using the social cost of carbon.", "monetary_role": "outcome_amount_claim", "amount": { "source_text": "US$149–696 million", "value": null, "lower": "149000000", "upper": "696000000", "qualifier": "range", "currency": "USD", "currency_basis": "explicit", "price_basis": "Social-cost-of-carbon valuation; valuation and price year are not specified in the checked abstract." }, "additional_amounts": [], "financial_type": "monetized_social_benefit", "amount_basis": "valuation", "period": "Comparison with pre-2016 deforestation; the checked abstract does not specify the valuation horizon.", "unit": "Modeled value of avoided deforestation in the African study sample.", "beneficiary": "People benefiting from avoided climate damage; no direct cash recipient.", "payer": "Not applicable to a social-cost valuation.", "realization": "modeled_valuation", "accounting_boundary": "Modeled climate value, not cash income, market revenue, or a fully costed net-benefit estimate.", "comparator": "Deforestation associated with subscriptions versus the study counterfactual. Alert availability alone had no significant effect.", "ai_attribution": "GLAD’s machine-learning forest-change alerts are inputs to the intervention. The evaluation concerns subscriptions and subsequent responses, not model performance in isolation.", "ai_attribution_status": "ai_component_supported", "net_benefit_established": false, "sources": [ { "url": "https://ageconsearch.umn.edu/record/304666/files/18339%20manuscript%20ABSTRACT%20ONLY.pdf", "title": "Near Real-Time Forest Monitoring Can Decrease Deforestation", "quote": "we estimate a value of the alert system between USD 149 million and 696 million.", "date": null, "primary": true, "written_by": "unknown", "reviewed_at": "2026-10-23", "review_method": "full_text", "locator": "Author abstract, single-page PDF" } ], "limitations": [ "Only the author abstract was checked for this monetary range; the full valuation method remains unreviewed.", "The reported deforestation effect was specific to Africa, with no detected effect on other continents." ], "overlap_flags": [ "The interval represents alternative valuation assumptions, not two additive benefits." ], "curation_status": "curated", "source_review_status": "primary_source_checked", "case_link_status": "matched", "review_note": "The cited monetary statement and its accounting boundary were checked. This is not a certification of net benefit or AI-specific causation.", "reviewed_on": "2026-10-23", "legacy_audit": { "monetary_role": "outcome_amount_claim", "money_type": "other", "money_subtype": "monetized environmental and social benefit", "claim_status": "retrospective_modeled_valuation", "currency": "USD", "currency_basis": "explicit", "unit_basis": "avoided deforestation in the African study region", "time_basis": "Study includes a 2011-2016 baseline; the valuation window is not explicit in value.", "time_basis_completeness": "incomplete_window", "monetary_comparator_assessment": "The account describes a counterfactual for deforestation; no cash-flow comparator.", "ai_and_value_scope": "Valuation concerns the alert system with human uptake and response, not AI in isolation.", "gross_net_status": "not_explicitly_established", "gross_net_interpretation": "Social-cost-of-carbon valuation; net costs and broader welfare effects are not supplied.", "net_benefit_established": false, "comparability_flags": [ "nonmarket_valuation", "range_not_point_estimate", "valuation_assumption_sensitive", "whole_program_attribution" ], "data_gaps": [ "Valuation period", "Carbon quantity and social cost of carbon assumptions", "Price year and discount rate", "Program costs and uncertainty method" ], "extraction_note": "This is a monetary valuation of an environmental outcome, not cash saved or revenue received.", "source_review_required": true, "id": "AIBD-0105", "title": "GLAD deforestation alert subscriptions reduce deforestation probability in Africa by 18%", "value": { "amount": "between US$149 million and US$696 million", "what": "value of avoided deforestation in Africa, priced at the social cost of carbon", "who": "society, through avoided carbon emissions", "estimate": true, "quote": "Calculated using the social cost of carbon for avoided deforestation in Africa, we estimate the alert system’s value to be between US$149 million and US$696 million.", "url": "https://www.nature.com/articles/s41558-020-00956-w" }, "sourceURL": "https://www.nature.com/articles/s41558-020-00956-w", "date_observed_as_stored": "2021-01-04", "existing_estimate_flag": true, "existing_comparator_present": true, "source_verification_status": "not_reopened_or_verified_in_this_audit", "reviewed_existing_fields": [ "value", "title", "metric", "comparator", "account", "ai_role", "date_observed" ], "source_review_priority": "standard", "unit_basis_present": true, "amount_specific_measurement_window_fully_explicit": false, "original_quote_and_source_link_present": true }, "eligible": true }, { "id": "DIV-0006", "case_id": "AIBD-0193", "title": "Avoided East African locust losses", "summary": "FAO estimates that the locust response avoided $1.56 billion in cereal and milk losses.", "monetary_role": "outcome_amount_claim", "amount": { "source_text": "US$1.56 billion", "value": "1560000000", "lower": null, "upper": null, "qualifier": "exact", "currency": "USD", "currency_basis": "explicit", "price_basis": "Nominal source amount; price year not separately stated." }, "additional_amounts": [], "financial_type": "avoided_loss", "amount_basis": "avoided_expenditure", "period": "East African response reported May 6, 2021; the amount-specific start date is unstated.", "unit": "Cereal and milk losses avoided across the regional response.", "beneficiary": "Farmers, livestock keepers, and food consumers in affected East African countries.", "payer": "Not applicable to avoided production losses.", "realization": "retrospective_estimate", "accounting_boundary": "Estimated gross production losses avoided by the response; response costs are not deducted.", "comparator": "Estimated losses without the control response; calculation details are not given in this account.", "ai_attribution": "PlantVillage AI helped clean monitoring data within a larger response involving field teams, aircraft, forecasts, and pest control. The AI-specific increment is unknown.", "ai_attribution_status": "ai_component_supported", "net_benefit_established": false, "sources": [ { "url": "https://www.fao.org/newsroom/story/A-high-tech-response-is-helping-countries-win-the-battle-against-Desert-Locusts/en", "title": "A high-tech response is helping countries win the battle against Desert Locusts", "quote": "avoiding USD 1.56 billion in cereal and milk losses.", "date": "2021-05-06", "primary": true, "written_by": "deployer", "reviewed_at": "2026-10-23", "review_method": "full_text", "locator": null } ], "limitations": [ "FAO reports a whole-response counterfactual estimate, not cash received or an independently isolated AI effect." ], "overlap_flags": [ "The cereal and milk quantities describe the same avoided-loss estimate and are not additional monetary gains." ], "curation_status": "curated", "source_review_status": "primary_source_checked", "case_link_status": "matched", "review_note": "The cited monetary statement and its accounting boundary were checked. This is not a certification of net benefit or AI-specific causation.", "reviewed_on": "2026-10-23", "legacy_audit": { "monetary_role": "outcome_amount_claim", "money_type": "avoided_loss", "money_subtype": "agricultural production losses", "claim_status": "retrospective_counterfactual_estimate", "currency": "USD", "currency_basis": "explicit", "unit_basis": "entire East African locust-control campaign", "time_basis": "Campaign beginning in 2020, reported in May 2021; precise valuation cutoff needs checking.", "time_basis_completeness": "incomplete_window", "monetary_comparator_assessment": "Assumed no-control-operations counterfactual; no quantified monetary baseline in the structured comparator.", "ai_and_value_scope": "The amount belongs to the whole control operation, including aircraft and field action, to which AI-supported information contributed.", "gross_net_status": "not_explicitly_established", "gross_net_interpretation": "Gross avoided production loss; campaign costs are not deducted.", "net_benefit_established": false, "comparability_flags": [ "whole_program_attribution", "counterfactual_estimate", "agricultural_valuation", "not_AI_only" ], "data_gaps": [ "Valuation method and price year", "Precise campaign period and geographic coverage", "AI contribution", "Cost of control operations" ], "extraction_note": "The amount must not be described as the app or AI alone generating USD 1.56 billion.", "source_review_required": true, "id": "AIBD-0193", "title": "eLocust3m and AI data cleaning feed FAO's East Africa locust control, which may have averted USD 1.56 billion in losses", "value": { "amount": "USD 1.56 billion", "what": "cereal and milk losses avoided by the locust control operations in East Africa that the app and AI data cleaning fed", "who": "East African farmers and herders (the food security of 36.6 million people)", "estimate": true, "quote": "In East Africa locust control operations have prevented the loss of 4 million tonnes of cereal and 800 million litres of milk production, while protecting the food security of 36.6 million people and avoiding USD 1.56 billion in cereal and milk losses.", "url": "https://www.fao.org/newsroom/story/A-high-tech-response-is-helping-countries-win-the-battle-against-Desert-Locusts/en" }, "sourceURL": "https://www.fao.org/newsroom/story/A-high-tech-response-is-helping-countries-win-the-battle-against-Desert-Locusts/en", "date_observed_as_stored": "2021-05-06", "existing_estimate_flag": true, "existing_comparator_present": true, "source_verification_status": "not_reopened_or_verified_in_this_audit", "reviewed_existing_fields": [ "value", "title", "metric", "comparator", "account", "ai_role", "date_observed" ], "source_review_priority": "standard", "unit_basis_present": true, "amount_specific_measurement_window_fully_explicit": false, "original_quote_and_source_link_present": true }, "eligible": true }, { "id": "DIV-0007", "case_id": "AIBD-0337", "title": "myTax prompts and estimated tax revenue", "summary": "ATO estimates that adjustments following myTax prompts affected 2020 tax revenue by about A$37 million.", "monetary_role": "outcome_amount_claim", "amount": { "source_text": "About A$37 million", "value": "37000000", "lower": null, "upper": null, "qualifier": "approximately", "currency": "AUD", "currency_basis": "context", "price_basis": "Nominal source amount; price year not separately stated." }, "additional_amounts": [], "financial_type": "revenue", "amount_basis": "incremental_change", "period": "Australian tax time 2020.", "unit": "Aggregate adjustments following myTax prompts.", "beneficiary": "Australian public revenue.", "payer": "Taxpayers adjusting their returns.", "realization": "retrospective_estimate", "accounting_boundary": "Estimated tax-revenue impact, not confirmed cash collection, net program return, or aggregate social benefit.", "comparator": "Amounts entered before versus after prompts; an untreated causal comparison is not supplied.", "ai_attribution": "ATO describes nearest-neighbor comparisons and other checks that prompted review. Their separate contributions are not quantified.", "ai_attribution_status": "ai_component_supported", "net_benefit_established": false, "sources": [ { "url": "https://www.ato.gov.au/api/public/content/0-55fbf5c1-cd9e-49b9-8c8a-42b607e83a36", "title": "ATO: How we use data and analytics", "quote": "These prompts caused taxpayers to make adjustments estimated to have a revenue impact of around $37 million.", "date": null, "primary": true, "written_by": "deployer", "reviewed_at": "2026-10-23", "review_method": "indexed_excerpt", "locator": "Helping you make better choices" } ], "limitations": [ "The relevant official excerpt was available through search indexing; direct retrieval returned an access error.", "The source states an estimated revenue impact rather than collected taxes." ], "overlap_flags": [ "The separate $42 million of automated franking-credit refunds is not part of this estimate." ], "curation_status": "curated", "source_review_status": "primary_excerpt_checked", "case_link_status": "matched", "review_note": "The cited monetary statement and its accounting boundary were checked. This is not a certification of net benefit or AI-specific causation.", "reviewed_on": "2026-10-23", "legacy_audit": { "monetary_role": "outcome_amount_claim", "money_type": "revenue", "money_subtype": "tax assessment or revenue impact", "claim_status": "retrospective_estimate", "currency": "AUD", "currency_basis": "inferred_from_Australian_program", "unit_basis": "taxpayer adjustments during one tax season", "time_basis": "Tax time 2020", "time_basis_completeness": "explicit_window", "monetary_comparator_assessment": "No separate control or monetary baseline is stated.", "ai_and_value_scope": "Attributed by ATO to adjustments following prompts; collection and incremental causality are not established.", "gross_net_status": "not_explicitly_established", "gross_net_interpretation": "Estimated gross revenue impact; not demonstrated net collected cash.", "net_benefit_established": false, "comparability_flags": [ "assessed_not_collected", "currency_inferred", "operator_estimate", "no_control" ], "data_gaps": [ "Confirmed currency", "Tax assessments versus actual receipts", "Counterfactual changes without prompts", "Program costs" ], "extraction_note": "Exclude unrelated refund totals in the account from this claim.", "source_review_required": true, "id": "AIBD-0337", "title": "ATO's nearest-neighbor myTax prompts reached nearly 340,000 taxpayers at tax time 2020; resulting adjustments had an estimated $37 million revenue impact", "value": { "amount": "$37 million", "what": "revenue impact of the adjustments taxpayers made after myTax prompts at tax time 2020 (ATO's own estimate)", "who": "the Australian Taxation Office (Australian Government revenue)", "estimate": true, "quote": "During tax time in 2020, nearly 340,000 taxpayers (around 7.5% of myTax users), received a pop-up message through myTax suggesting they review a specific label. These prompts caused taxpayers to make adjustments estimated to have a revenue impact of around $37 million.", "url": "https://web.archive.org/web/20210714025448/https://www.ato.gov.au/About-ATO/Commitments-and-reporting/Information-and-privacy/How-we-use-data-and-analytics/" }, "sourceURL": "https://web.archive.org/web/20210714025448/https://www.ato.gov.au/About-ATO/Commitments-and-reporting/Information-and-privacy/How-we-use-data-and-analytics/", "date_observed_as_stored": "2021-07-14", "existing_estimate_flag": true, "existing_comparator_present": false, "source_verification_status": "not_reopened_or_verified_in_this_audit", "reviewed_existing_fields": [ "value", "title", "metric", "comparator", "account", "ai_role", "date_observed" ], "source_review_priority": "standard", "unit_basis_present": true, "amount_specific_measurement_window_fully_explicit": true, "original_quote_and_source_link_present": true }, "eligible": true }, { "id": "DIV-0010", "case_id": "AIBD-0031", "title": "Lower household medical spending after flood alerts", "summary": "A randomized study estimates that community dissemination of AI flood alerts reduced household illness-treatment spending by roughly INR6,500.", "monetary_role": "outcome_amount_claim", "amount": { "source_text": "About INR6,500 per household", "value": "6500", "lower": null, "upper": null, "qualifier": "approximately", "currency": "INR", "currency_basis": "explicit", "price_basis": "Nominal source amount; price year not separately stated." }, "additional_amounts": [], "financial_type": "cash_saving", "amount_basis": "incremental_change", "period": "2022–2023 flood-season study; the prose does not isolate the monetary recall window.", "unit": "Estimated difference per household in illness-treatment spending.", "beneficiary": "Households in treatment communities in Bihar, India.", "payer": "Households paying for illness treatment.", "realization": "retrospective_estimate", "accounting_boundary": "Estimated household medical-expenditure reduction, not a full net-welfare or program-profit estimate.", "comparator": "Randomized community dissemination versus control communities, both with access to the AI forecasting system.", "ai_attribution": "The randomized intervention is dissemination of AI forecasts, not AI versus no AI. Its effect cannot be relabeled as the forecasting model’s isolated contribution.", "ai_attribution_status": "ai_component_supported", "net_benefit_established": false, "sources": [ { "url": "https://bpb-us-w2.wpmucdn.com/campuspress.yale.edu/dist/7/2986/files/2024/04/Google_Floods-bcdf49042e23059a.pdf", "title": "Forecasting Fate: Experimental Evaluation of a Flood Early Warning System", "quote": "households in treatment areas incurred roughly INR 6,500 less on illness treatment", "date": "2024-04-03", "primary": true, "written_by": "independent", "reviewed_at": "2026-10-23", "review_method": "full_text", "locator": "PDF page 6; medical-expenditure result and Table 4" } ], "limitations": [ "The manuscript reports preliminary estimates and no evidence of broader improvements in economic well-being.", "The reported amount is a study estimate, not a guaranteed saving for each household." ], "overlap_flags": [ "The paper’s benefit-cost ratio uses these medical savings; do not count it as another benefit." ], "curation_status": "curated", "source_review_status": "primary_source_checked", "case_link_status": "matched", "review_note": "The cited monetary statement and its accounting boundary were checked. This is not a certification of net benefit or AI-specific causation.", "reviewed_on": "2026-10-23", "legacy_audit": { "monetary_role": "outcome_amount_claim", "money_type": "cash_saving", "money_subtype": "household medical expenditure", "claim_status": "study_estimate_of_observed_spending_difference", "currency": "INR", "currency_basis": "explicit", "unit_basis": "per household over a flood season", "time_basis": "2022 and 2023 monsoon study; the amount-specific season requires confirmation.", "time_basis_completeness": "partial_window", "monetary_comparator_assessment": "Randomized comparison for community dissemination; control households already had access to AI phone alerts.", "ai_and_value_scope": "Incremental effect of community dissemination added to forecast access, not AI forecasts versus no AI.", "gross_net_status": "not_explicitly_established", "gross_net_interpretation": "Household expenditure saving before program costs; a separate benefit-cost ratio does not make the headline amount net.", "net_benefit_established": false, "comparability_flags": [ "per_household", "study_estimate_despite_false_flag", "comparator_already_has_AI", "season_specific", "increment_is_delivery_layer" ], "data_gaps": [ "Which season and sample support INR 6,500", "Confidence interval and attrition", "How illness spending was measured", "Program cost and ratio assumptions" ], "extraction_note": "A measured study estimate still has sampling uncertainty; estimate=false should not imply an exact cash-accounting total.", "source_review_required": true, "id": "AIBD-0031", "title": "Randomized evaluation of Google AI flood alerts with community dissemination in Bihar, India (2022–2023 monsoons)", "value": { "amount": "INR 6,500", "what": "less spent on illness treatment per household over the flood season", "who": "treatment households in the Bihar study panchayats", "estimate": false, "quote": "As a result, households in treatment areas incurred roughly INR 6,500 less on illness treatment (a 32.87% decrease). In fact, based on the estimated reduction in medical expenditures alone, we estimate that for every $1 spent on the program, the intervention would generate a minimum benefit of $30 during a severe flooding season.", "url": "https://bpb-us-w2.wpmucdn.com/campuspress.yale.edu/dist/7/2986/files/2024/04/Google_Floods-bcdf49042e23059a.pdf" }, "sourceURL": "https://bpb-us-w2.wpmucdn.com/campuspress.yale.edu/dist/7/2986/files/2024/04/Google_Floods-bcdf49042e23059a.pdf", "date_observed_as_stored": "2022-06-01", "existing_estimate_flag": false, "existing_comparator_present": true, "source_verification_status": "not_reopened_or_verified_in_this_audit", "reviewed_existing_fields": [ "value", "title", "metric", "comparator", "account", "ai_role", "date_observed" ], "source_review_priority": "standard", "unit_basis_present": true, "amount_specific_measurement_window_fully_explicit": false, "original_quote_and_source_link_present": true }, "eligible": true }, { "id": "DIV-0014", "case_id": "AIBD-0013", "title": "Vesuvius Challenge grand prize", "summary": "The competition awarded $700,000 to the three-person team that used machine learning to read a carbonized scroll.", "monetary_role": "recipient_transfer", "amount": { "source_text": "US$700,000", "value": "700000", "lower": null, "upper": null, "qualifier": "exact", "currency": "USD", "currency_basis": "context", "price_basis": "Nominal source amount; price year not separately stated." }, "additional_amounts": [], "financial_type": "recipient_transfer", "amount_basis": "transfer", "period": "Grand-prize announcement on February 5, 2024.", "unit": "One prize shared by a three-person team.", "beneficiary": "Youssef Nader, Luke Farritor, and Julian Schilliger.", "payer": "Vesuvius Challenge prize fund.", "realization": "awarded", "accounting_boundary": "Gross prize award to recipients, not new aggregate wealth or research profit.", "comparator": "No cash counterfactual is stated.", "ai_attribution": "Machine learning supported the winning scroll-reading work. The award is a financial consequence of that work, without an isolated AI share.", "ai_attribution_status": "ai_component_supported", "net_benefit_established": false, "sources": [ { "url": "https://scrollprize.org/grandprize", "title": "Vesuvius Challenge 2023 Grand Prize awarded", "quote": "the Vesuvius Challenge Grand Prize of $700,000 is awarded to a team of three", "date": "2024-02-05", "primary": true, "written_by": "deployer", "reviewed_at": "2026-10-23", "review_method": "full_text", "locator": null } ], "limitations": [ "The announcement establishes an award, not completed payment or the winners’ net proceeds." ], "overlap_flags": [ "One shared prize, not $700,000 per team member." ], "curation_status": "curated", "source_review_status": "primary_source_checked", "case_link_status": "matched", "review_note": "The cited monetary statement and its accounting boundary were checked. This is not a certification of net benefit or AI-specific causation.", "reviewed_on": "2026-10-23", "legacy_audit": { "monetary_role": "recipient_transfer", "money_type": "other", "money_subtype": "prize award to winning researchers", "claim_status": "awarded", "currency": "USD", "currency_basis": "inferred_from_competition_context", "unit_basis": "one team award shared among three winners", "time_basis": "Award announced February 5, 2024", "time_basis_completeness": "dated_one_off", "monetary_comparator_assessment": "The comparator measures scroll text recovered, not financial gain.", "ai_and_value_scope": "Prize awarded for AI-assisted work; award is not a valuation of recovered knowledge.", "gross_net_status": "not_explicitly_established", "gross_net_interpretation": "Gross award to recipients; research costs and payer outlay are separate.", "net_benefit_established": false, "comparability_flags": [ "recipient_transfer", "prize_not_research_value", "gross_award" ], "data_gaps": [ "Payment confirmation and recipient split if relevant", "Research costs", "Keep award distinct from monetized cultural value" ], "extraction_note": "Potentially useful in a recipient-benefit or research-incentive view, but not a quantified improvement in the cost or value of reading the scroll.", "source_review_required": true, "id": "AIBD-0013", "title": "Vesuvius Challenge 2023: machine-learning ink detection reads over 2,000 characters from unopened Herculaneum scroll PHerc.Paris.4", "value": { "amount": "$700,000", "what": "Vesuvius Challenge grand prize awarded for the winning submission that read the scroll", "who": "Youssef Nader, Luke Farritor and Julian Schilliger, paid by the Vesuvius Challenge", "estimate": false, "quote": "The results of this review were clear and unanimous: the Vesuvius Challenge Grand Prize of $700,000 is awarded to a team of three for their excellent submission. Congratulations to Youssef Nader, Luke Farritor, and Julian Schilliger!", "url": "https://scrollprize.org/grandprize" }, "sourceURL": "https://scrollprize.org/grandprize", "date_observed_as_stored": "2024-02-05", "existing_estimate_flag": false, "existing_comparator_present": true, "source_verification_status": "not_reopened_or_verified_in_this_audit", "reviewed_existing_fields": [ "value", "title", "metric", "comparator", "account", "ai_role", "date_observed" ], "source_review_priority": "standard", "unit_basis_present": true, "amount_specific_measurement_window_fully_explicit": true, "original_quote_and_source_link_present": true }, "eligible": true }, { "id": "DIV-0017", "case_id": "AIBD-0095", "title": "Flood-triggered cash transfers in Nigeria", "summary": "GiveDirectly reports that it paid $105 per person in an AI-informed flood-response pilot in Kogi, Nigeria.", "monetary_role": "recipient_transfer", "amount": { "source_text": "US$105 per recipient", "value": "105", "lower": null, "upper": null, "qualifier": "exact", "currency": "USD", "currency_basis": "context", "price_basis": "Nominal source amount; price year not separately stated." }, "additional_amounts": [ { "id": "ngn-equivalent", "amount": { "source_text": "NGN177,866 per recipient", "value": "177866", "lower": null, "upper": null, "qualifier": "exact", "currency": "NGN", "currency_basis": "explicit", "price_basis": "Nominal source amount; price year not separately stated." }, "relationship": "source_equivalent", "equivalent_to": "primary", "equivalence_note": "Reported equivalent; later naira payments may differ.", "period": "August 29 to October 24, 2024, for the first transfer.", "unit": "Per recipient, first transfer.", "realization": "paid", "accounting_boundary": "Recipient cash transfer. A transfer is not a net increase in societal resources or an estimate of avoided flood loss.", "sources": [ { "url": "https://www.aljazeera.com/features/2024/9/21/could-ai-save-nigerians-from-devastating-floods", "title": "Could AI save Nigerians from devastating floods?", "quote": "177,866 naira ($105)", "date": "2024-09-21", "primary": false, "written_by": "independent", "reviewed_at": "2026-10-23", "review_method": "full_text", "locator": "First anticipatory payment in Kogi" } ], "source_review_status": "source_checked", "review_note": "Denomination checked." } ], "financial_type": "recipient_transfer", "amount_basis": "transfer", "period": "August 29 to October 24, 2024, for the first transfer.", "unit": "Per recipient, first transfer.", "beneficiary": "Registered recipients in six wards of Kogi, Nigeria.", "payer": "GiveDirectly and its funders.", "realization": "paid", "accounting_boundary": "Recipient cash transfer. A transfer is not a net increase in societal resources or an estimate of avoided flood loss.", "comparator": "The amount is not a comparison with aid delivered without AI.", "ai_attribution": "AI forecasts informed targeting and payment triggers alongside local observations and human verification.", "ai_attribution_status": "ai_component_supported", "net_benefit_established": false, "sources": [ { "url": "https://www.givedirectly.org/flood-forecast-ai", "title": "How AI helped send cash just before floods hit in Nigeria", "quote": "we sent $105 to over 4,600 individuals", "date": "2025-06-27", "primary": true, "written_by": "deployer", "reviewed_at": "2026-10-23", "review_method": "full_text", "locator": null } ], "limitations": [ "The detailed account says that 3,250 people received early payments and another 1,400 were paid after the October 21 peak; the summary overstates early coverage.", "Transfer amount is implementer-reported." ], "overlap_flags": [ "A later $210 transfer is separate. Do not treat the $105 amount as a program total." ], "curation_status": "curated", "source_review_status": "primary_source_checked", "case_link_status": "matched", "review_note": "The cited monetary statement and its accounting boundary were checked. This is not a certification of net benefit or AI-specific causation.", "reviewed_on": "2026-10-23", "legacy_audit": { "monetary_role": "recipient_transfer", "money_type": "other", "money_subtype": "anticipatory cash transfer", "claim_status": "reported_paid_transfer_with_timing_dispute", "currency": "USD", "currency_basis": "currency_inferred_from_implementer_context", "unit_basis": "per recipient; the account reports ambiguity between individual and household", "time_basis": "Transfers August-October 2024; a separate $210 transfer followed.", "time_basis_completeness": "explicit_window", "monetary_comparator_assessment": "Income and food-security before/after survey; no controlled monetary-impact estimate.", "ai_and_value_scope": "AI contributed to targeting and timing; the cash is the program transfer, not the incremental value of AI.", "gross_net_status": "not_explicitly_established", "gross_net_interpretation": "Gross recipient transfer and donor outlay; not net social gain.", "net_benefit_established": false, "comparability_flags": [ "recipient_transfer", "intervention_input", "recipient_denominator_ambiguous", "late_payment_subset", "no_control" ], "data_gaps": [ "Individual versus household denominator", "Which payments preceded flooding", "Amount-specific cohort", "AI timing benefit relative to unconditional or later cash", "Program costs" ], "extraction_note": "The $105 can be tracked as money received, while the separate income change is a distinct outcome claim.", "source_review_required": true, "id": "AIBD-0095", "title": "GiveDirectly paid anticipatory cash in Kogi State, Nigeria, on Google Flood Hub forecast triggers (August to October 2024)", "value": { "amount": "$105", "what": "anticipatory cash transfer per recipient before the flood peak (a second $210 transfer followed)", "who": "over 4,600 flood-exposed residents of Ibaji, Kogi State", "estimate": false, "quote": "Once forecasted flood thresholds were met, we sent $105 to over 4,600 individuals before the flood peak.", "url": "https://www.givedirectly.org/flood-forecast-ai/" }, "sourceURL": "https://www.givedirectly.org/flood-forecast-ai/", "date_observed_as_stored": "2024-08-29", "existing_estimate_flag": false, "existing_comparator_present": true, "source_verification_status": "not_reopened_or_verified_in_this_audit", "reviewed_existing_fields": [ "value", "title", "metric", "comparator", "account", "ai_role", "date_observed" ], "source_review_priority": "standard", "unit_basis_present": true, "amount_specific_measurement_window_fully_explicit": true, "original_quote_and_source_link_present": true }, "eligible": true }, { "id": "DIV-0018", "case_id": "AIBD-0144", "title": "Blackpool road-maintenance savings", "summary": "Blackpool Council reports that technology-assisted repairs cost over £1 million less than its estimate for traditional methods.", "monetary_role": "outcome_amount_claim", "amount": { "source_text": "Over £1 million", "value": "1000000", "lower": null, "upper": null, "qualifier": "more_than", "currency": "GBP", "currency_basis": "context", "price_basis": "Nominal source amount; price year not separately stated." }, "additional_amounts": [], "financial_type": "cash_saving", "amount_basis": "incremental_change", "period": "Since summer 2019 to the council account; the endpoint is not specified in the text.", "unit": "5,145 potholes in Blackpool’s North Shore.", "beneficiary": "Blackpool Council.", "payer": "Council road-maintenance budget; savings are avoided expenditure.", "realization": "retrospective_estimate", "accounting_boundary": "Reported repair-cost difference against an estimated traditional-method cost, without a fully costed AI-only comparison.", "comparator": "About £1.5 million under traditional methods versus just under £450,000 reported cost.", "ai_attribution": "AI-assisted condition surveys supported a wider maintenance approach. The source does not isolate AI from the repair and workflow changes.", "ai_attribution_status": "ai_component_supported", "net_benefit_established": false, "sources": [ { "url": "https://blackpoolunlimited.com/artificial-intelligence-boosts-road-maintenance-efficiency/", "title": "Artificial intelligence boosts road maintenance efficiency", "quote": "save over £1m resulting in a final cost of just under £450k.", "date": null, "primary": true, "written_by": "deployer", "reviewed_at": "2026-10-23", "review_method": "full_text", "locator": "Council account of Project Amber’s North Shore repairs" } ], "limitations": [ "Council-reported costs and a modeled traditional-method baseline." ], "overlap_flags": [ "The two underlying cost figures form this difference and must not be added as separate benefits." ], "curation_status": "curated", "source_review_status": "primary_source_checked", "case_link_status": "matched", "review_note": "The cited monetary statement and its accounting boundary were checked. This is not a certification of net benefit or AI-specific causation.", "reviewed_on": "2026-10-23", "legacy_audit": { "monetary_role": "outcome_amount_claim", "money_type": "cash_saving", "money_subtype": "road-repair expenditure avoided", "claim_status": "reported_saving_against_estimated_traditional_cost", "currency": "GBP", "currency_basis": "symbol_and_UK_context", "unit_basis": "5,145 potholes in North Shore", "time_basis": "Since summer 2019; exact end of repair cohort not stated on the cited value page.", "time_basis_completeness": "incomplete_window", "monetary_comparator_assessment": "Value/account compare about £1.5m traditional cost with under £450k actual cost; the structured comparator instead concerns compensation payouts.", "ai_and_value_scope": "The entire Project Amber process combines imagery, prioritization and surface treatment.", "gross_net_status": "not_explicitly_established", "gross_net_interpretation": "Gross repair saving relative to a traditional-cost estimate; total technology/program costs are unspecified.", "net_benefit_established": false, "comparability_flags": [ "whole_program_attribution", "comparator_field_different_outcome", "estimated_counterfactual", "cohort_end_unclear" ], "data_gaps": [ "Repair-cohort period", "Basis of traditional-cost estimate", "Like-for-like repair quality and scope", "All implementation and ongoing costs" ], "extraction_note": "Do not combine repair savings with reduced compensation payouts without distinct claims and overlap checks.", "source_review_required": true, "id": "AIBD-0144", "title": "Blackpool Council's Project Amber imagery and AI road surveys: 5,145 potholes filled for about £450,000 and pothole claim payouts down to £719", "value": { "amount": "over £1m", "what": "saved on filling 5,145 potholes in North Shore, final cost just under £450k against £1.5m by traditional methods", "who": "Blackpool Council", "estimate": false, "quote": "Blackpool Council has managed to save over £1m resulting in a final cost of just under £450k.", "url": "https://blackpoolunlimited.com/artificial-intelligence-boosts-road-maintenance-efficiency/" }, "sourceURL": "https://blackpoolunlimited.com/artificial-intelligence-boosts-road-maintenance-efficiency/", "date_observed_as_stored": "2024-09-26", "existing_estimate_flag": false, "existing_comparator_present": true, "source_verification_status": "not_reopened_or_verified_in_this_audit", "reviewed_existing_fields": [ "value", "title", "metric", "comparator", "account", "ai_role", "date_observed" ], "source_review_priority": "standard", "unit_basis_present": true, "amount_specific_measurement_window_fully_explicit": false, "original_quote_and_source_link_present": true }, "eligible": true }, { "id": "DIV-0019", "case_id": "AIBD-0173", "title": "Treasury check-fraud recoveries", "summary": "Treasury reports that machine learning helped identify check fraud associated with $1 billion recovered in fiscal 2024.", "monetary_role": "outcome_amount_claim", "amount": { "source_text": "US$1 billion", "value": "1000000000", "lower": null, "upper": null, "qualifier": "exact", "currency": "USD", "currency_basis": "context", "price_basis": "Nominal source amount; price year not separately stated." }, "additional_amounts": [], "financial_type": "recovered_funds", "amount_basis": "incremental_change", "period": "U.S. fiscal year 2024.", "unit": "Treasury check-fraud recoveries across the program.", "beneficiary": "U.S. Treasury and affected federal programs.", "payer": "Recovered fraud proceeds; individual payers are not identified.", "realization": "reported_recovery", "accounting_boundary": "Gross recovered funds. Detection, recovery, and operating costs are not deducted.", "comparator": "The release does not estimate recoveries without machine learning.", "ai_attribution": "Treasury states that machine learning expedited check-fraud identification. The amount is not an estimate of the incremental gain from AI alone.", "ai_attribution_status": "ai_component_supported", "net_benefit_established": false, "sources": [ { "url": "https://home.treasury.gov/news/press-releases/jy2650", "title": "Treasury announces enhanced fraud detection processes, including machine learning AI", "quote": "Expediting the identification of Treasury check fraud with machine learning AI resulting in $1 billion in recovery.", "date": "2024-10-17", "primary": true, "written_by": "deployer", "reviewed_at": "2026-10-23", "review_method": "full_text", "locator": null } ], "limitations": [ "Agency-reported recovery, without a counterfactual AI-only estimate or full program costs." ], "overlap_flags": [ "Component of Treasury’s broader fiscal-2024 recovery and prevention total; do not add it to that total." ], "curation_status": "curated", "source_review_status": "primary_source_checked", "case_link_status": "matched", "review_note": "The cited monetary statement and its accounting boundary were checked. This is not a certification of net benefit or AI-specific causation.", "reviewed_on": "2026-10-23", "legacy_audit": { "monetary_role": "outcome_amount_claim", "money_type": "avoided_loss", "money_subtype": "recovered check fraud", "claim_status": "reported_recovery", "currency": "USD", "currency_basis": "inferred_from_US_program", "unit_basis": "annual machine-learning check-fraud recovery", "time_basis": "FY2024: October 2023-September 2024", "time_basis_completeness": "explicit_window", "monetary_comparator_assessment": "Structured comparator compares the full prevention/recovery program across years, not the $1bn AI component.", "ai_and_value_scope": "Treasury explicitly attributes this component to machine-learning identification; incremental causality remains unevaluated.", "gross_net_status": "not_explicitly_established", "gross_net_interpretation": "Gross recovered funds; recovery and system costs unspecified.", "net_benefit_established": false, "comparability_flags": [ "component_vs_program_comparator", "recovered_not_prevented", "operator_claim", "overlap_with_4_billion_total" ], "data_gaps": [ "Recovery accounting and audit", "Program costs", "Equivalent prior-year ML recovery", "Incremental contribution relative to investigators" ], "extraction_note": "The $1bn is a component of the greater-than-$4bn total, not an amount to add to that total.", "source_review_required": true, "id": "AIBD-0173", "title": "Treasury's machine-learning check-fraud detection recovers $1 billion in FY2024", "value": { "amount": "$1 billion", "what": "Treasury check fraud recovered in FY2024 through machine-learning identification", "who": "U.S. Treasury, on behalf of taxpayers", "estimate": false, "quote": "Expediting the identification of Treasury check fraud with machine learning AI resulting in $1 billion in recovery.", "url": "https://home.treasury.gov/news/press-releases/jy2650" }, "sourceURL": "https://home.treasury.gov/news/press-releases/jy2650", "date_observed_as_stored": "2024-10-17", "existing_estimate_flag": false, "existing_comparator_present": true, "source_verification_status": "not_reopened_or_verified_in_this_audit", "reviewed_existing_fields": [ "value", "title", "metric", "comparator", "account", "ai_role", "date_observed" ], "source_review_priority": "standard", "unit_basis_present": true, "amount_specific_measurement_window_fully_explicit": true, "original_quote_and_source_link_present": true }, "eligible": true }, { "id": "DIV-0031", "case_id": "AIBD-0184", "title": "Estimated Universal Credit overpayments avoided", "summary": "The NAO reports DWP’s estimate that its advances model avoided £4.4 million of overpayments over three fiscal years.", "monetary_role": "outcome_amount_claim", "amount": { "source_text": "Estimated £4.4 million", "value": "4400000", "lower": null, "upper": null, "qualifier": "approximately", "currency": "GBP", "currency_basis": "context", "price_basis": "Nominal source amount; price year not separately stated." }, "additional_amounts": [], "financial_type": "avoided_loss", "amount_basis": "avoided_expenditure", "period": "U.K. fiscal years 2022–23 through 2024–25.", "unit": "Aggregate Universal Credit advances model estimate.", "beneficiary": "U.K. Department for Work and Pensions and public funds.", "payer": "DWP benefit expenditure that would otherwise have been overpaid.", "realization": "retrospective_estimate", "accounting_boundary": "Estimated gross overpayments avoided. DWP could not separate this model’s costs from wider analytics costs.", "comparator": "DWP’s estimate of payments that otherwise would have been made; method is not detailed in the cited passage.", "ai_attribution": "The model flagged higher-risk applications for staff assessment. The estimate concerns the combined screening and human-review process.", "ai_attribution_status": "ai_component_supported", "net_benefit_established": false, "sources": [ { "url": "https://www.nao.org.uk/wp-content/uploads/2025/10/Tackling-benefit-overpayments-due-to-fraud-and-error.pdf", "title": "Tackling benefit overpayments due to fraud and error", "quote": "the model generated savings of £4.4 million that would otherwise have been overpaid.", "date": "2025-10-22", "primary": true, "written_by": "independent", "reviewed_at": "2026-10-23", "review_method": "full_text", "locator": "PDF page 31, paragraph 2.11" } ], "limitations": [ "An independent report relays DWP’s estimate; it does not establish a cost-adjusted AI return." ], "overlap_flags": [ "The model estimate must not be added to broader DWP totals that already include it." ], "curation_status": "curated", "source_review_status": "primary_source_checked", "case_link_status": "matched", "review_note": "The cited monetary statement and its accounting boundary were checked. This is not a certification of net benefit or AI-specific causation.", "reviewed_on": "2026-10-23", "legacy_audit": { "monetary_role": "outcome_amount_claim", "money_type": "avoided_loss", "money_subtype": "benefit overpayments avoided", "claim_status": "retrospective_estimate", "currency": "GBP", "currency_basis": "symbol_and_UK_context", "unit_basis": "DWP Universal Credit advance model", "time_basis": "2022-23 through 2024-25", "time_basis_completeness": "explicit_window", "monetary_comparator_assessment": "Randomized control sample supports risk-identification comparison; the monetary calculation is not set out in the existing fields.", "ai_and_value_scope": "Model prioritizes human caseworker review.", "gross_net_status": "not_explicitly_established", "gross_net_interpretation": "Gross overpayments avoided; account explicitly says model costs cannot be disaggregated.", "net_benefit_established": false, "comparability_flags": [ "gross_costs_not_separable", "operator_estimate_in_audit", "multi_year_cumulative", "distributional_effects" ], "data_gaps": [ "Saving-calculation method", "Treatment/control monetary outcomes", "Costs if separable", "False-positive and delay effects" ], "extraction_note": "Independent reporting of an agency estimate should not be mislabeled as an independently verified net saving.", "source_review_required": true, "id": "AIBD-0184", "title": "DWP's Universal Credit advances machine-learning model saved an estimated £4.4 million from 2022–23 to 2024–25", "value": { "amount": "£4.4 million", "what": "advances that would otherwise have been overpaid, saved from 2022-23 to 2024-25", "who": "Department for Work & Pensions", "estimate": true, "quote": "DWP estimates that, from 2022-23 to 2024-25, the model generated savings of £4.4 million that would otherwise have been overpaid.", "url": "https://www.nao.org.uk/wp-content/uploads/2025/10/Tackling-benefit-overpayments-due-to-fraud-and-error.pdf" }, "sourceURL": "https://www.nao.org.uk/wp-content/uploads/2025/10/Tackling-benefit-overpayments-due-to-fraud-and-error.pdf", "date_observed_as_stored": "2025-10-22", "existing_estimate_flag": true, "existing_comparator_present": true, "source_verification_status": "not_reopened_or_verified_in_this_audit", "reviewed_existing_fields": [ "value", "title", "metric", "comparator", "account", "ai_role", "date_observed" ], "source_review_priority": "standard", "unit_basis_present": true, "amount_specific_measurement_window_fully_explicit": true, "original_quote_and_source_link_present": true }, "eligible": true }, { "id": "DIV-0032", "case_id": "AIBD-0163", "title": "Quartz Solar annual imbalance-cost estimate", "summary": "Open Climate Fix estimates that Quartz Solar avoids at least £30 million in electricity imbalance costs per year.", "monetary_role": "outcome_amount_claim", "amount": { "source_text": "At least £30 million per year", "value": "30000000", "lower": null, "upper": null, "qualifier": "at_least", "currency": "GBP", "currency_basis": "explicit", "price_basis": "Nominal source amount; price year not separately stated." }, "additional_amounts": [], "financial_type": "cash_saving", "amount_basis": "avoided_expenditure", "period": "Annualized estimate published November 6, 2025; no completed measurement year is specified.", "unit": "Annual electricity imbalance costs.", "beneficiary": "Great Britain’s electricity system and its cost bearers.", "payer": "Electricity balancing and imbalance settlement participants.", "realization": "annualized_estimate", "accounting_boundary": "Estimated annual gross operating-cost avoidance, without implementation and ongoing service costs.", "comparator": "Previous NESO forecasting tools; the monetary calculation is not provided.", "ai_attribution": "Quartz Solar combines machine learning, satellite imagery, and weather data within NESO’s operating workflow. Its developer supplies the monetary estimate.", "ai_attribution_status": "ai_component_supported", "net_benefit_established": false, "sources": [ { "url": "https://www.openclimatefix.org/insights/neso-adopts-ai-solar-forecasting-control-room", "title": "National Energy System Operator adopts AI solar forecasting", "quote": "helping to avoid at least £30 million in imbalance costs each year", "date": "2025-11-06", "primary": true, "written_by": "makers", "reviewed_at": "2026-10-23", "review_method": "full_text", "locator": null } ], "limitations": [ "An annualized developer estimate is not a verified cash saving for a completed year." ], "overlap_flags": [ "Future scenarios or cumulative savings cannot be added to this annual rate without a defined period." ], "curation_status": "curated", "source_review_status": "primary_source_checked", "case_link_status": "matched", "review_note": "The cited monetary statement and its accounting boundary were checked. This is not a certification of net benefit or AI-specific causation.", "reviewed_on": "2026-10-23", "legacy_audit": { "monetary_role": "outcome_amount_claim", "money_type": "cash_saving", "money_subtype": "electricity imbalance costs", "claim_status": "annualized_developer_estimate", "currency": "GBP", "currency_basis": "explicit", "unit_basis": "per year at current adoption", "time_basis": "Annualized estimate announced November 2025; measured savings interval unspecified.", "time_basis_completeness": "annualized_without_window", "monetary_comparator_assessment": "Forecast errors reportedly halved; no operator monetary baseline.", "ai_and_value_scope": "Maker estimates value of an integrated forecast service; operator confirms deployment, not the saving figure.", "gross_net_status": "not_explicitly_established", "gross_net_interpretation": "Gross avoided imbalance cost; service and integration costs unstated.", "net_benefit_established": false, "comparability_flags": [ "annualized_not_observed_year", "developer_estimate", "operational_to_financial_conversion", "future_scenario_separate" ], "data_gaps": [ "Calculation from forecast error to cost", "Operator confirmation", "Observed period", "Costs and consumer pass-through" ], "extraction_note": "Keep the £30m annual estimate separate from the £150m/year conditional 2035 scenario.", "source_review_required": true, "id": "AIBD-0163", "title": "Open Climate Fix's Quartz Solar goes live in NESO's control room and may avoid £30m a year in imbalance costs", "value": { "amount": "GBP 30 million", "what": "imbalance cost avoided per year", "who": "Great Britain's electricity system operator NESO and, through balancing costs, consumers", "estimate": true, "quote": "Since adoption, Quartz Solar has halved forecasting errors, according to Open Climate Fix, equivalent to saving at least GBP 30 million in imbalance cost per year, with potential to increase to GBP 150 million ($197.0 million) per year in 2035 if government solar capacity targets are met.", "url": "https://www.pv-magazine.com/2025/11/07/ai-powered-solar-forecasting-helps-uk-grid-operator-reduce-balancing-costs/" }, "sourceURL": "https://www.pv-magazine.com/2025/11/07/ai-powered-solar-forecasting-helps-uk-grid-operator-reduce-balancing-costs/", "date_observed_as_stored": "2025-11-06", "existing_estimate_flag": true, "existing_comparator_present": true, "source_verification_status": "not_reopened_or_verified_in_this_audit", "reviewed_existing_fields": [ "value", "title", "metric", "comparator", "account", "ai_role", "date_observed" ], "source_review_priority": "standard", "unit_basis_present": true, "amount_specific_measurement_window_fully_explicit": false, "original_quote_and_source_link_present": true }, "eligible": true }, { "id": "DIV-0036", "case_id": "AIBD-0262", "title": "Payment for Nimbus’s TYK2 program", "summary": "Takeda’s annual securities report states that it paid Nimbus $4 billion upfront for the subsidiary owning the TYK2 program.", "monetary_role": "commercial_transaction", "amount": { "source_text": "US$4 billion", "value": "4000000000", "lower": null, "upper": null, "qualifier": "exact", "currency": "USD", "currency_basis": "explicit", "price_basis": "Nominal source amount; price year not separately stated." }, "additional_amounts": [], "financial_type": "commercial_receipt", "amount_basis": "transaction", "period": "Acquisition closed February 8, 2023; payment is reported in the fiscal-2023 annual securities report filed June 26, 2024.", "unit": "One acquisition’s upfront consideration.", "beneficiary": "Nimbus Therapeutics and the relevant sellers.", "payer": "Takeda Pharmaceutical Company.", "realization": "paid", "accounting_boundary": "Gross acquisition consideration, not drug revenue, seller profit, clinical benefit, or newly created societal wealth.", "comparator": "No estimate of the transaction value without AI-assisted design is supplied.", "ai_attribution": "The acquisition concerns the NDI-034858/TAK-279 program linked to the AIBD computational-design case. The transaction source does not value AI’s incremental contribution.", "ai_attribution_status": "ai_component_supported", "net_benefit_established": false, "sources": [ { "url": "https://assets-dam.takeda.com/image/upload/Global/Investor/ASR/Y2023/E_FY23_ASR.pdf", "title": "Takeda annual securities report for the year ended March 31, 2024", "quote": "we paid Nimbus USD 4.0 billion upfront following the closing of the transaction", "date": "2024-06-26", "primary": true, "written_by": "deployer", "reviewed_at": "2026-10-23", "review_method": "full_text", "locator": "Printed page 50, material contracts" } ], "limitations": [ "The 2023 transaction predates the later clinical milestones in the parent AIBD record.", "No AI-specific premium or net economic benefit is established." ], "overlap_flags": [ "Two contingent $1 billion milestones are excluded; the $4 billion payment is not the $6 billion maximum deal value." ], "curation_status": "curated", "source_review_status": "primary_source_checked", "case_link_status": "matched", "review_note": "The cited monetary statement and its accounting boundary were checked. This is not a certification of net benefit or AI-specific causation.", "reviewed_on": "2026-10-23", "legacy_audit": { "monetary_role": "commercial_transaction", "money_type": "revenue", "money_subtype": "upfront sale of drug rights", "claim_status": "announced_transaction_payment", "currency": "USD", "currency_basis": "inferred_from_US_biopharma_reporting", "unit_basis": "one rights acquisition", "time_basis": "Deal announced December 13, 2022; main clinical outcome occurs in 2026.", "time_basis_completeness": "dated_one_off", "monetary_comparator_assessment": "Clinical comparator is efficacy, not value or development cost.", "ai_and_value_scope": "Sale of a compound developed with computational assistance; the AI-attributable increment in commercial value is unknown.", "gross_net_status": "not_explicitly_established", "gross_net_interpretation": "Gross transaction consideration, not profit, patient benefit, or net social value.", "net_benefit_established": false, "comparability_flags": [ "commercial_transfer", "transaction_vs_clinical_outcome", "payment_completion_not_checked", "AI_share_unresolved" ], "data_gaps": [ "Deal closing and actual payment", "Development and acquisition costs", "AI contribution to transaction value", "Separate optional milestones from upfront payment" ], "extraction_note": "The $4bn may belong in a commercial-outcomes view, but it is not a monetary measure of the clinical benefit.", "source_review_required": true, "id": "AIBD-0262", "title": "Zasocitinib, computationally designed by Nimbus with Schrodinger's platform, beats deucravacitinib in phase 3; FDA priority review", "value": { "amount": "$4 billion", "what": "upfront payment for rights to the computationally designed compound", "who": "Nimbus (seller), paid by Takeda", "estimate": false, "quote": "The Japanese pharmaceutical company will pay Nimbus $4 billion upfront for rights to the medicine, which is in clinical testing for multiple autoimmune conditions.", "url": "https://www.biopharmadive.com/news/nimbus-takeda-tyk2-inhibitor-drug-deal/638614/" }, "sourceURL": "https://www.biopharmadive.com/news/nimbus-takeda-tyk2-inhibitor-drug-deal/638614/", "date_observed_as_stored": "2026-09-30", "existing_estimate_flag": false, "existing_comparator_present": true, "source_verification_status": "not_reopened_or_verified_in_this_audit", "reviewed_existing_fields": [ "value", "title", "metric", "comparator", "account", "ai_role", "date_observed" ], "source_review_priority": "standard", "unit_basis_present": true, "amount_specific_measurement_window_fully_explicit": true, "original_quote_and_source_link_present": true }, "eligible": true } ]