{ "id": "AIBD-0189", "title": "Google FINDER-flagged restaurants are 3.1 times as likely to be unsafe in Las Vegas and Chicago inspections", "summary": "Health departments inspected 132 FINDER-flagged restaurants in 2016–2017; 52.3% were unsafe versus 24.7% of 10,786 other inspections (odds ratio 3.06).", "date_observed": "2018-11-06", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "Google built FINDER, a machine-learned model that estimates the fraction of people who visited a restaurant and later searched for terms indicative of food poisoning. It uses anonymous and aggregated web search and location data. It was deployed in Las Vegas between May and August 2016 (5,038 inspections completed, 61 prompted by FINDER). In Chicago it ran between November 2016 and March 2017 (5,880 inspections, 71 prompted by FINDER). Each morning the Southern Nevada Health District and the Chicago Department of Public Health received a list of restaurants identified by FINDER. They dispatched inspectors unaware of whether an inspection was prompted by the model. Of the 132 FINDER-identified restaurants, 69 (52.3%) were deemed unsafe, against 2,662 of 10,786 (24.7%) baseline restaurants, an odds ratio of 3.06 (95% CI 2.14–4.35). Against routine inspections alone the unsafe rate was 22.7% and the odds ratio 3.16. In 38% of cases the restaurant potentially causing illness was not the last one visited. The sources give deployment months but no exact day.", "category": "benefit created", "unexpected": false, "purpose": "Identify restaurants likely to be causing foodborne illness from anonymized, aggregated web-search and location data so that health departments can target inspections.", "system": "FINDER (machine-learned foodborne illness detection model)", "model_version": null, "developer": "Google", "deployer": "Southern Nevada Health District (Las Vegas) and Chicago Department of Public Health", "location": "Las Vegas and Chicago, United States", "domain": "Food safety / public health", "segment": "Restaurant inspection targeting", "benefit_type": "Improved Task Accuracy", "taxonomy_note": null, "registry_ids": {}, "ratings": [ { "group": "organizational", "depth": 2, "reason": "69 of 132 FINDER-prompted inspections (52.3%) found an unsafe restaurant versus 2,662 of 10,786 (24.7%) baseline inspections. Odds ratio 3.06 (95% CI 2.14–4.35), adjusted for city and risk level." }, { "group": "societal", "depth": null, "reason": "The paper argues that corrective actions “would prevent” spread of foodborne illness but documents no reduction in illness." } ], "ai_role": { "status": "assessed", "statement": "FINDER generated the daily restaurant lists. Inspectors did not know the source of the prompt. The comparison is observational (FINDER-prompted versus other inspections during the same period), not randomized. Google employees conceived and implemented the model and co-authored the peer-reviewed evaluation. Health department staff oversaw implementation." }, "evidence": [ { "kind": "peer-reviewed study", "citation": "Sadilek A, Caty S, DiPrete L, Mansour R, Schenk T Jr, Bergtholdt M, Jha A, Ramaswami P, Gabrilovich E. Machine-learned epidemiology: real-time detection of foodborne illness at scale. npj Digital Medicine 1:36 (2018). doi:10.1038/s41746-018-0045-1", "title": "Machine-learned epidemiology: real-time detection of foodborne illness at scale", "url": "https://doi.org/10.1038/s41746-018-0045-1", "date": "2018-11-06", "written_by": "makers", "quote": "Of all the restaurants identified by FINDER, 52.3% were deemed unsafe upon inspection, compared to 24.7% for BASELINE restaurants (Table 2)." } ], "replication": { "produced_by": [ "Aggregated web search and location logs covering a city's restaurant patrons", "Health departments able to act on a daily list", "Inspection outcome records to compare against routine and complaint-based inspections" ], "must_hold": [ "Access to anonymized, aggregated search and location data held by Google", "Tolerance of the incubation-period lag before inference", "Inspectors unaware of the prompt source" ], "echoes": [ { "date": "2016-02-18", "setting": "Las Vegas, Twitter-based nEmesis controlled experiment from 2 January 2015", "model_version": "nEmesis (predecessor)", "outcome": "recurred", "source": "Sadilek et al. 2016, AAAI, doi:10.1609/aaai.v30i2.19072" } ] }, "paired_incident": null, "headline": "Google FINDER flags restaurants 3.1 times as likely to be unsafe", "metric": "52.3% were unsafe versus 24.7% of 10,786 other inspections (odds ratio 3.06)", "comparator": { "against": "all other restaurant inspections conducted in the two cities during the deployment period (BASELINE)", "before": "24.7% of BASELINE restaurants deemed unsafe", "after": "52.3% of FINDER-identified restaurants deemed unsafe (3.06 times as likely)", "basis": "measured", "quote": "Of all the restaurants identified by FINDER, 52.3% were deemed unsafe upon inspection, compared to 24.7% for BASELINE restaurants (Table 2). We used binomial logistic regression to determine the odds ratio of being unsafe for restaurants in the FINDER and BASELINE groups. The former were 3.06 times (95% CI: 2.14–4.35) as likely to be unsafe as the latter, when accounting for restaurant risk level and city in our models (p < 0.001, Table 2).", "url": "https://www.nature.com/articles/s41746-018-0045-1" }, "later": { "status": "unknown", "date": null, "note": "No report of FINDER being deployed again or adopted by other health departments after the 2018 paper was found.", "quote": null, "url": null }, "value": null, "developer_country": "United States", "deployer_country": "United States", "unrated_because": null, "brief_notes": null, "brief": null, "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Improved Task Accuracy under Trustible's full taxonomy of 23 types; it was Improved Organizational Efficiency among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what the benefit is measured against from the cited sources.", "source": "https://www.nature.com/articles/s41746-018-0045-1", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/Google; https://www.southernnevadahealthdistrict.org/about-us/maps/southern-nevada-health-district-main-facility/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" }, { "date": "2026-10-22", "change": "Organization name standardized to Google.", "source": null, "editor": "AIBD" } ], "confidence": "claimed", "shade": "low", "claim_status": "The makers' claim", "claim_basis": "Sources: the makers' peer-reviewed study. The cited evidence includes no independent source and no finding by a regulator or court.", "profile": { "case_id": "AIBD-0189", "catalog_version": "1.0.0", "source_fingerprint": "ce8fdf81a945bc5914e0c1f010971ff94700c3abe73393a746e2dd9f530829b7", "collection_refs": [ { "collection_id": "aibd", "version": "snapshot-8bf7cfec9cb185aa" }, { "collection_id": "aibd", "version": "2026-10-25" } ], "source_ids": [ "AIBD-0189-source-0001" ], "families": { "strategic assessments": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "evaluations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "monetary observations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "institutional decisions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] } }, "fields": { "actors[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "developer": "Google", "deployer": "Southern Nevada Health District (Las Vegas) and Chicago Department of Public Health" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "developer", "deployer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "actors[].ownership_control": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "ai_contribution": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ai_role": { "status": "assessed", "statement": "FINDER generated the daily restaurant lists. Inspectors did not know the source of the prompt. The comparison is observational (FINDER-prompted versus other inspections during the same period), not randomized. Google employees conceived and implemented the model and co-authored the peer-reviewed evaluation. Health department staff oversaw implementation." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ai_role" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "ai_relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "category": "benefit created", "ai_role": { "status": "assessed", "statement": "FINDER generated the daily restaurant lists. Inspectors did not know the source of the prompt. The comparison is observational (FINDER-prompted versus other inspections during the same period), not randomized. Google employees conceived and implemented the model and co-authored the peer-reviewed evaluation. Health department staff oversaw implementation." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ai_role", "category" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].alternatives": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].assessor_date": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].depth": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "organizational", "depth": 2, "reason": "69 of 132 FINDER-prompted inspections (52.3%) found an unsafe restaurant versus 2,662 of 10,786 (24.7%) baseline inspections. 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It uses anonymous and aggregated web search and location data. It was deployed in Las Vegas between May and August 2016 (5,038 inspections completed, 61 prompted by FINDER). In Chicago it ran between November 2016 and March 2017 (5,880 inspections, 71 prompted by FINDER). Each morning the Southern Nevada Health District and the Chicago Department of Public Health received a list of restaurants identified by FINDER. They dispatched inspectors unaware of whether an inspection was prompted by the model. Of the 132 FINDER-identified restaurants, 69 (52.3%) were deemed unsafe, against 2,662 of 10,786 (24.7%) baseline restaurants, an odds ratio of 3.06 (95% CI 2.14–4.35). Against routine inspections alone the unsafe rate was 22.7% and the odds ratio 3.16. In 38% of cases the restaurant potentially causing illness was not the last one visited. 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Odds ratio 3.06 (95% CI 2.14–4.35), adjusted for city and risk level." }, { "group": "societal", "reason": "The paper argues that corrective actions “would prevent” spread of foodborne illness but documents no reduction in illness." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].status": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].tradeoffs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].unexpected": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "unexpected": false, "purpose": "Identify restaurants likely to be causing foodborne illness from anonymized, aggregated web-search and location data so that health departments can target inspections." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; 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