{
  "id": "AIBD-0190",
  "title": "Chicago's food inspection forecasting model finds critical violations 7.5 days earlier in a double-blind retrodiction",
  "summary": "Double-blind retrodiction of 1,637 Chicago inspections (Sept-Oct 2014): the model would have found 69% of critical-violation sites in the first half of the work versus 55%, 7.5 days earlier.",
  "date_observed": "2015-05-19",
  "date_basis": "unassessed",
  "date_precision": "unassessed",
  "benefit_status": null,
  "source_collection": null,
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  "account": "Chicago's Department of Innovation and Technology and Department of Public Health built a model. Pro bono data scientists from Allstate Insurance and the Civic Consulting Alliance helped. It assigns each of the city's 15,000+ food establishments a likelihood of a critical violation. Predictors include previous critical or serious violations, three-day average high temperature, nearby garbage and sanitation complaints and facility type. Others are nearby burglaries, licenses, time since last inspection, length of operation and the inspector assigned. In a double-blind retrodiction, the Department of Public Health inspected 1,637 establishments in its normal order during September and October 2014. Of these, 258 (almost 16%) had at least one critical violation, 141 (55%) found in the first month. Re-ordering the same inspections by the model's probabilities would have found 178 (69%) in the first half of the work, on average 7.5 days earlier. Another 37 would have been cited in the first month. The Harvard Ash Center's Data-Smart City Solutions wrote up the pilot on 19 May 2015. The city page is undated. The code is open source in R.",
  "category": "general benefit",
  "unexpected": false,
  "purpose": "Forecast which Chicago food establishments are most likely to have critical sanitation violations so that inspectors visit them first.",
  "system": "Food Inspection Forecasting (predictive analytics model, open-sourced in R)",
  "model_version": null,
  "developer": "City of Chicago Department of Innovation and Technology with the Department of Public Health, Allstate Insurance data scientists and the Civic Consulting Alliance",
  "deployer": "Chicago Department of Public Health",
  "location": "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": null,
      "reason": "The sources document a retrospective simulation (69% vs 55% found in the first half; 7.5 days earlier) and no live-deployment outcome."
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      "depth": null,
      "reason": "Earlier discovery “potentially after patrons became ill” is stated as a rationale. No illness outcome is documented."
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  "ai_role": {
    "status": "assessed",
    "statement": "Developer and deployer are the same body. The City of Chicago built the model in-house with pro bono partners and the Department of Public Health is the user. The evaluation re-scored inspections after they were completed, so the model did not change any inspection in the pilot. Model type is not stated in the sources."
  },
  "evidence": [
    {
      "kind": "report",
      "citation": "City of Chicago. Food Inspection Forecasting: Optimizing Inspections with Analytics. chicago.github.io (undated; copyright 2014-2017).",
      "title": "Food Inspection Forecasting - City of Chicago",
      "url": "https://chicago.github.io/food-inspections-evaluation/",
      "date": null,
      "written_by": "makers",
      "quote": "Over the two month pilot, establishments with violations were found, on average, 7 and a half days earlier."
    },
    {
      "kind": "news report",
      "citation": "Thornton S. Delivering Faster Results with Food Inspection Forecasting: Chicago's Analytics-Driven Plan to Prevent Foodborne Illness. Data-Smart City Solutions, Harvard Ash Center, 19 May 2015.",
      "title": "Delivering Faster Results with Food Inspection Forecasting",
      "url": "http://datasmart.ash.harvard.edu/news/article/delivering-faster-results-with-food-inspection-forecasting-631",
      "date": "2015-05-19",
      "written_by": "independent",
      "quote": "When using an analytics-based procedure, Chicago was able to discover critical violations, on average, seven days earlier than if they had used the traditional inspection procedure."
    },
    {
      "kind": "preprint",
      "citation": "Kannan V, Shapiro MA, Bilgic M. Hindsight Analysis of the Chicago Food Inspection Forecasting Model. arXiv:1910.04906, submitted 10 October 2019.",
      "title": "Hindsight Analysis of the Chicago Food Inspection Forecasting Model",
      "url": "https://arxiv.org/abs/1910.04906",
      "date": "2019-10-10",
      "written_by": "independent",
      "quote": null
    }
  ],
  "replication": {
    "produced_by": [
      "Open inspection, 311 complaint, business license and weather data on the city's data portal",
      "Fewer than three dozen inspectors for 15,000+ establishments, so ordering matters",
      "A fixed two-month inspection record to re-score"
    ],
    "must_hold": [
      "The predictor set (prior violations, nearby sanitation complaints, facility type, days since last inspection, inspector) remains available",
      "A critical-violation base rate near 15%"
    ],
    "echoes": []
  },
  "paired_incident": null,
  "headline": "Chicago's model finds critical violations 7.5 days earlier",
  "metric": "69% of critical-violation sites in the first half of the work versus 55%, 7.5 days earlier",
  "comparator": {
    "against": "the Department of Public Health's normal inspection order during the September-October 2014 pilot",
    "before": "55 percent (141 establishments) of those with critical violations found in the first month during normal operations",
    "after": "69 percent (178 establishments) found in the first half of work; violations found on average 7 and a half days earlier",
    "basis": "measured",
    "quote": "During the simulation, 69 percent of inspections—178 establishments—with critical violations were found during the first half of work, compared to 55 percent during normal operations. Over the two month pilot, establishments with violations were found, on average, 7 and a half days earlier.",
    "url": "https://chicago.github.io/food-inspections-evaluation/"
  },
  "later": {
    "status": "scaled",
    "date": "2019-10-10",
    "note": "An independent 2019 hindsight analysis states that CDPH deployed the model in 2015 to schedule inspections of Chicago's 15,000-plus food establishments and released the training data and code.",
    "quote": "In 2015, CDPH deployed a machine learning model to schedule inspections of establishments based on their likelihood to commit critical food code violations. The City of Chicago released the training data and source code for the model, allowing anyone to examine the model.",
    "url": "https://arxiv.org/abs/1910.04906"
  },
  "value": null,
  "developer_country": "United States",
  "deployer_country": "United States",
  "unrated_because": null,
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    {
      "date": "2026-10-20",
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      "editor": "AIBD"
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      "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, what happened since from the cited sources.",
      "source": "https://chicago.github.io/food-inspections-evaluation/; https://arxiv.org/abs/1910.04906",
      "editor": "AIBD"
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      "date": "2026-10-21",
      "change": "Country of the developer and the deployer added from the organization's own site or a registry.",
      "source": "https://github.com/Chicago; https://en.wikipedia.org/wiki/Chicago_Department_of_Public_Health",
      "editor": "AIBD"
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      "date": "2026-10-21",
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        },
        "provenance": {
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          "source_refs": []
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        "review_state": "record_mapped",
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        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial"
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      "outcomes[].duration": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial"
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      "outcomes[].id": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial"
      },
      "outcomes[].stakeholder_group": {
        "state": "value_present",
        "value": {
          "kind": "record_fields",
          "fields": {
            "ratings": [
              {
                "group": "organizational",
                "reason": "The sources document a retrospective simulation (69% vs 55% found in the first half; 7.5 days earlier) and no live-deployment outcome."
              },
              {
                "group": "societal",
                "reason": "Earlier discovery “potentially after patrons became ill” is stated as a rationale. No illness outcome is documented."
              }
            ]
          }
        },
        "provenance": {
          "kind": "legacy_record",
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          "source_refs": []
        },
        "review_state": "record_mapped",
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      },
      "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,
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          }
        },
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          "kind": "legacy_record",
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          ],
          "source_refs": []
        },
        "review_state": "record_mapped",
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        "state": "value_present",
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          "kind": "record_fields",
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            "location": "Chicago, United States",
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          }
        },
        "provenance": {
          "kind": "legacy_record",
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          "record_fields": [
            "location",
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            "deployer_country"
          ],
          "source_refs": []
        },
        "review_state": "record_mapped",
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      },
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          "kind": "record_fields",
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            "purpose": "Forecast which Chicago food establishments are most likely to have critical sanitation violations so that inspectors visit them first."
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        },
        "provenance": {
          "kind": "legacy_record",
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          "record_fields": [
            "purpose"
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        "review_state": "record_mapped",
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        "state": "value_present",
        "value": {
          "kind": "record_fields",
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            "date_observed": "2015-05-19",
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              {
                "date": "2026-10-20",
                "change": "Record drafted from the cited sources.",
                "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.",
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                "editor": "AIBD"
              },
              {
                "date": "2026-10-21",
                "change": "Added what the benefit is measured against, what happened since from the cited sources.",
                "source": "https://chicago.github.io/food-inspections-evaluation/; https://arxiv.org/abs/1910.04906",
                "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://github.com/Chicago; https://en.wikipedia.org/wiki/Chicago_Department_of_Public_Health",
                "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.",
                "editor": "AIBD"
              }
            ]
          }
        },
        "provenance": {
          "kind": "legacy_record",
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          "source_refs": []
        },
        "review_state": "record_mapped",
        "coverage": "partial"
      },
      "replication.attempts[]": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial"
      },
      "replication.conditions": {
        "state": "value_present",
        "value": {
          "kind": "record_fields",
          "fields": {
            "replication": {
              "produced_by": [
                "Open inspection, 311 complaint, business license and weather data on the city's data portal",
                "Fewer than three dozen inspectors for 15,000+ establishments, so ordering matters",
                "A fixed two-month inspection record to re-score"
              ],
              "must_hold": [
                "The predictor set (prior violations, nearby sanitation complaints, facility type, days since last inspection, inspector) remains available",
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              ],
              "echoes": []
            }
          }
        },
        "provenance": {
          "kind": "legacy_record",
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          "source_refs": []
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        "review_state": "record_mapped",
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      },
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        "value": {
          "kind": "record_fields",
          "fields": {
            "revisions": [
              {
                "date": "2026-10-20",
                "change": "Record drafted from the cited sources.",
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              },
              {
                "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, what happened since from the cited sources.",
                "source": "https://chicago.github.io/food-inspections-evaluation/; https://arxiv.org/abs/1910.04906",
                "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://github.com/Chicago; https://en.wikipedia.org/wiki/Chicago_Department_of_Public_Health",
                "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.",
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        },
        "provenance": {
          "kind": "legacy_record",
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          "source_refs": []
        },
        "review_state": "record_mapped",
        "coverage": "partial"
      },
      "sector": {
        "state": "value_present",
        "value": {
          "kind": "record_fields",
          "fields": {
            "domain": "Food safety / public health",
            "segment": "Restaurant inspection targeting"
          }
        },
        "provenance": {
          "kind": "legacy_record",
          "basis": "Preserved from the existing AIBD record; external sources were not rechecked.",
          "record_fields": [
            "domain",
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          ],
          "source_refs": []
        },
        "review_state": "record_mapped",
        "coverage": "partial"
      },
      "sources[].access_version": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial"
      },
      "sources[].citation_locator": {
        "state": "value_present",
        "value": {
          "kind": "record_fields",
          "fields": {
            "evidence": [
              {
                "kind": "report",
                "citation": "City of Chicago. Food Inspection Forecasting: Optimizing Inspections with Analytics. chicago.github.io (undated; copyright 2014-2017).",
                "title": "Food Inspection Forecasting - City of Chicago",
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                "quote": "Over the two month pilot, establishments with violations were found, on average, 7 and a half days earlier."
              },
              {
                "kind": "news report",
                "citation": "Thornton S. Delivering Faster Results with Food Inspection Forecasting: Chicago's Analytics-Driven Plan to Prevent Foodborne Illness. Data-Smart City Solutions, Harvard Ash Center, 19 May 2015.",
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                "url": "http://datasmart.ash.harvard.edu/news/article/delivering-faster-results-with-food-inspection-forecasting-631",
                "date": "2015-05-19",
                "written_by": "independent",
                "quote": "When using an analytics-based procedure, Chicago was able to discover critical violations, on average, seven days earlier than if they had used the traditional inspection procedure."
              },
              {
                "kind": "preprint",
                "citation": "Kannan V, Shapiro MA, Bilgic M. Hindsight Analysis of the Chicago Food Inspection Forecasting Model. arXiv:1910.04906, submitted 10 October 2019.",
                "title": "Hindsight Analysis of the Chicago Food Inspection Forecasting Model",
                "url": "https://arxiv.org/abs/1910.04906",
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            ]
          }
        },
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            "AIBD-0190-source-0001",
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          "role": "Existing evidence items, in their original order"
        },
        "provenance": {
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        "review_state": "record_mapped",
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      "sources[].language": {
        "state": "not_assessed",
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        "value": {
          "kind": "record_fields",
          "fields": {
            "evidence": [
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                "kind": "report",
                "citation": "City of Chicago. Food Inspection Forecasting: Optimizing Inspections with Analytics. chicago.github.io (undated; copyright 2014-2017).",
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              },
              {
                "kind": "news report",
                "citation": "Thornton S. Delivering Faster Results with Food Inspection Forecasting: Chicago's Analytics-Driven Plan to Prevent Foodborne Illness. Data-Smart City Solutions, Harvard Ash Center, 19 May 2015.",
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                "url": "http://datasmart.ash.harvard.edu/news/article/delivering-faster-results-with-food-inspection-forecasting-631",
                "date": "2015-05-19",
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              },
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                "written_by": "independent"
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            ]
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          "kind": "legacy_record",
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        },
        "review_state": "record_mapped",
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      },
      "sources[].upstream_sources": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial"
      },
      "strategic_assessments[]": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial",
        "family": "strategic assessments"
      },
      "summary": {
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        "value": {
          "kind": "record_fields",
          "fields": {
            "summary": "Double-blind retrodiction of 1,637 Chicago inspections (Sept-Oct 2014): the model would have found 69% of critical-violation sites in the first half of the work versus 55%, 7.5 days earlier.",
            "account": "Chicago's Department of Innovation and Technology and Department of Public Health built a model. Pro bono data scientists from Allstate Insurance and the Civic Consulting Alliance helped. It assigns each of the city's 15,000+ food establishments a likelihood of a critical violation. Predictors include previous critical or serious violations, three-day average high temperature, nearby garbage and sanitation complaints and facility type. Others are nearby burglaries, licenses, time since last inspection, length of operation and the inspector assigned. In a double-blind retrodiction, the Department of Public Health inspected 1,637 establishments in its normal order during September and October 2014. Of these, 258 (almost 16%) had at least one critical violation, 141 (55%) found in the first month. Re-ordering the same inspections by the model's probabilities would have found 178 (69%) in the first half of the work, on average 7.5 days earlier. Another 37 would have been cited in the first month. The Harvard Ash Center's Data-Smart City Solutions wrote up the pilot on 19 May 2015. The city page is undated. The code is open source in R."
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        "provenance": {
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          "source_refs": []
        },
        "review_state": "record_mapped",
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      "systems[].autonomy_permissions": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial"
      },
      "systems[].component_role": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial"
      },
      "systems[].deployment_configuration": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial"
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      "systems[].inputs_outputs": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial"
      },
      "systems[].model_name": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial"
      },
      "systems[].model_version": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial"
      },
      "systems[].product_name": {
        "state": "value_present",
        "value": {
          "kind": "record_fields",
          "fields": {
            "system": "Food Inspection Forecasting (predictive analytics model, open-sourced in R)"
          }
        },
        "provenance": {
          "kind": "legacy_record",
          "basis": "Preserved from the existing AIBD record; external sources were not rechecked.",
          "record_fields": [
            "system"
          ],
          "source_refs": []
        },
        "review_state": "record_mapped",
        "coverage": "partial"
      },
      "systems[].provider": {
        "state": "value_present",
        "value": {
          "kind": "structured",
          "data": {
            "application_developer": "City of Chicago Department of Innovation and Technology with the Department of Public Health, Allstate Insurance data scientists and the Civic Consulting Alliance"
          }
        },
        "provenance": {
          "kind": "legacy_record",
          "basis": "Mapped from the existing case description without a new external source review.",
          "record_fields": [
            "developer"
          ],
          "source_refs": []
        },
        "review_state": "record_mapped",
        "coverage": "partial"
      },
      "taxonomy_crosswalks[]": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial"
      },
      "title": {
        "state": "value_present",
        "value": {
          "kind": "record_fields",
          "fields": {
            "title": "Chicago's food inspection forecasting model finds critical violations 7.5 days earlier in a double-blind retrodiction",
            "headline": "Chicago's model finds critical violations 7.5 days earlier"
          }
        },
        "provenance": {
          "kind": "legacy_record",
          "basis": "Preserved from the existing AIBD record; external sources were not rechecked.",
          "record_fields": [
            "title",
            "headline"
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          "source_refs": []
        },
        "review_state": "record_mapped",
        "coverage": "partial"
      },
      "version": {
        "state": "value_present",
        "value": {
          "kind": "identifiers",
          "identifiers": [
            "eae4cef5a98e27a8a86a802742962e062afaba659b07595ff176cb6c385044b7"
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          "role": "SHA-256 of normalized original case data"
        },
        "provenance": {
          "kind": "curatorial",
          "basis": "Deterministic content version assigned by the local migration.",
          "record_fields": [],
          "source_refs": []
        },
        "review_state": "record_mapped",
        "coverage": "complete"
      },
      "workflow": {
        "state": "value_present",
        "value": {
          "kind": "record_fields",
          "fields": {
            "account": "Chicago's Department of Innovation and Technology and Department of Public Health built a model. Pro bono data scientists from Allstate Insurance and the Civic Consulting Alliance helped. It assigns each of the city's 15,000+ food establishments a likelihood of a critical violation. Predictors include previous critical or serious violations, three-day average high temperature, nearby garbage and sanitation complaints and facility type. Others are nearby burglaries, licenses, time since last inspection, length of operation and the inspector assigned. In a double-blind retrodiction, the Department of Public Health inspected 1,637 establishments in its normal order during September and October 2014. Of these, 258 (almost 16%) had at least one critical violation, 141 (55%) found in the first month. Re-ordering the same inspections by the model's probabilities would have found 178 (69%) in the first half of the work, on average 7.5 days earlier. Another 37 would have been cited in the first month. The Harvard Ash Center's Data-Smart City Solutions wrote up the pilot on 19 May 2015. The city page is undated. The code is open source in R.",
            "replication": {
              "produced_by": [
                "Open inspection, 311 complaint, business license and weather data on the city's data portal",
                "Fewer than three dozen inspectors for 15,000+ establishments, so ordering matters",
                "A fixed two-month inspection record to re-score"
              ],
              "must_hold": [
                "The predictor set (prior violations, nearby sanitation complaints, facility type, days since last inspection, inspector) remains available",
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              ],
              "echoes": []
            }
          }
        },
        "provenance": {
          "kind": "legacy_record",
          "basis": "Preserved from the existing AIBD record; external sources were not rechecked.",
          "record_fields": [
            "account",
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          ],
          "source_refs": []
        },
        "review_state": "record_mapped",
        "coverage": "partial"
      }
    },
    "summary": {
      "total": 91,
      "addressed": 91,
      "known": 27,
      "reviewed": 0,
      "record_mapped": 27,
      "not_assessed": 64,
      "complete": false,
      "by_state": {
        "value_present": 27,
        "not_reported": 0,
        "unresolved": 0,
        "not_assessed": 64,
        "not_applicable": 0,
        "withheld": 0,
        "source_unavailable": 0
      }
    }
  }
}