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  "title": "Street Bump Road Defect App",
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     "timeline": {
      "started": 2011,
      "ended": 2014
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     "operator": "Mayor's Office of New Urban Mechanics, City of Boston; app built by Connected Bits with IDEO, building on research by Fabio Carrera and the Red Fish Group",
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      "text": "Boston's New Urban Mechanics office started Street Bump in 2011 and put the public version out in 2012",
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      "text": "an app that read a driving phone's accelerometer and GPS and filed road defects without anyone tapping anything",
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      "supportNote": "CNN describes the mechanism and the aggregation rule: the phone sends accelerometer data to a server that combines many phones, and 'If at least three people hit a bump in the same spot, the system recognizes it as a pothole.' GovTech describes the pipeline — data to the contractor's servers, likely problems submitted to the city through Open311 and classified as potholes to fix or known obstacles such as speed bumps.",
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      "text": "The city ended the project in 2014",
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      "text": "The city reports that sunken manhole covers caused about four times more bumps than potholes, that it fixed 1,250 of the worst covers by working with the utilities that own them, and that testing held false positives under 10%",
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      "text": "Boston never published how many reports the app generated, how many were confirmed, or whether repairs got faster",
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      "text": "Kate Crawford's 2013 Harvard Business Review essay made Street Bump the standard example of a sensing system's 'signal problem': a phone-and-car sensor samples people who own phones and cars",
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      "text": "That was an argument from smartphone ownership, not a measurement of Street Bump's own reports — Boston never published a neighbourhood breakdown, so the most-cited critique in civic sensing was never tested against the data it describes",
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      "caveat": "The FTC report PDF resisted text extraction, so it is verified at search level only and is not quoted as the city's own words. The claim that no breakdown was published is a negative finding across the sources reached.",
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      "title": "City of Boston — Street Bump (Mayor's Office of New Urban Mechanics)",
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       "value": "Independent account of the mechanism and the aggregation rule: the phone sends accelerometer data to a server that combines many phones, and 'If at least three people hit a bump in the same spot, the system recognizes it as a pothole.' Names Nigel Jacob of New Urban Mechanics as project lead and states the city's hope of replacing survey trucks with a real-time map.",
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      "title": "MIT Technology Review — Road Repair via Crowdsourcing (13 May 2011)",
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       "value": "Contemporary account of the algorithm competition: after the first build could not tell potholes from other bumps and produced too many false positives, the city turned to InnoCentive, with $25,000 in prize money donated by Liberty Mutual. The contest drew more than 700 solvers and 19 submitted solutions.",
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       "value": "A winning entry, from the winners' own institutional record: three winning teams were announced in December, two of them awarded $9,000 each. The method was wavelet analysis and Kruskal's algorithm — signal processing, not machine learning, which is why this entry carries ai: false.",
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      "title": "Kate Crawford — The Hidden Biases in Big Data, Harvard Business Review (1 April 2013)",
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       "value": "The origin of the critique, and the reason for the lesson's careful wording. This is a business-magazine essay, NOT a peer-reviewed paper: Crawford writes that 'StreetBump has a signal problem' because lower-income and older residents are less likely to own smartphones. The argument is drawn from smartphone-ownership demographics; neither this piece nor any source found analyses Street Bump's own reports by neighbourhood. HBR is paywalled to automated fetch; the passage is reproduced by the textbook below.",
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       "value": "Carries Crawford's Street Bump passage verbatim and shows how the case entered the algorithmic-fairness canon: 'Consider Street Bump, a project by the city of Boston to crowdsource data on potholes' … the data reflect 'the patterns of smartphone ownership, which are higher in wealthier parts of the city compared to lower-income areas and areas with large elderly populations.' The textbook presents it as an observation about the method, with no measurement of the project's data.",
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       "value": "The closest thing found to a documented Boston response: the FTC uses Street Bump as its worked example of a 'data desert', stating that once the team recognised that lower-income residents were less likely to carry smartphones, it recognised its data was not representative of road conditions across Boston. The report PDF resisted text extraction, so this is verified at search level only and is not quoted as the city's own words anywhere in this entry.",
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       "value": "Confirms that the pre-release version was run on city inspectors' vehicles before public release, and describes the pipeline: data to Connected Bits' servers, likely problems submitted to the city through Open311 and classified as potholes to fix or known obstacles such as speed bumps.",
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       "value": "Contemporary account of the algorithm competition: after the first build could not tell potholes from other bumps and produced too many false positives, the city turned to InnoCentive, with $25,000 in prize money donated by Liberty Mutual. The contest drew more than 700 solvers and 19 submitted solutions.",
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      "title": "Kate Crawford — The Hidden Biases in Big Data, Harvard Business Review (1 April 2013)",
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       "value": "The origin of the critique, and the reason for the lesson's careful wording. This is a business-magazine essay, NOT a peer-reviewed paper: Crawford writes that 'StreetBump has a signal problem' because lower-income and older residents are less likely to own smartphones. The argument is drawn from smartphone-ownership demographics; neither this piece nor any source found analyses Street Bump's own reports by neighbourhood. HBR is paywalled to automated fetch; the passage is reproduced by the textbook below.",
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       "type": "descriptive-source-note",
       "value": "Carries Crawford's Street Bump passage verbatim and shows how the case entered the algorithmic-fairness canon: 'Consider Street Bump, a project by the city of Boston to crowdsource data on potholes' … the data reflect 'the patterns of smartphone ownership, which are higher in wealthier parts of the city compared to lower-income areas and areas with large elderly populations.' The textbook presents it as an observation about the method, with no measurement of the project's data.",
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       "value": "The closest thing found to a documented Boston response: the FTC uses Street Bump as its worked example of a 'data desert', stating that once the team recognised that lower-income residents were less likely to carry smartphones, it recognised its data was not representative of road conditions across Boston. The report PDF resisted text extraction, so this is verified at search level only and is not quoted as the city's own words anywhere in this entry.",
       "exact": false
      },
      "preservation": {
       "archiveUrl": null,
       "archivedAt": null,
       "contentHash": null,
       "status": "not-captured"
      }
     },
     {
      "id": "src-20be4dee91740ccb",
      "title": "GovTech — Boston Testing App for Auto-Detecting Potholes",
      "url": "https://www.govtech.com/archive/boston-testing-app-for-auto-detecting-potholes.html",
      "publisher": {
       "name": null,
       "domain": "govtech.com",
       "classification": "not-classified"
      },
      "sourceType": "web-page",
      "language": "und",
      "role": "claim-evidence",
      "claimRefs": [
       "clm-ba4c49c4726d851e"
      ],
      "temporal": {
       "publishedAt": "2012-02-21",
       "updatedAt": null,
       "sourceCheckedAt": null,
       "recordLastVerifiedAt": "2026-07-26",
       "dossierCreatedAt": "2026-08-22",
       "retrievedAt": null,
       "status": "publication-and-record-dates-recorded"
      },
      "locator": {
       "type": "descriptive-source-note",
       "value": "Confirms that the pre-release version was run on city inspectors' vehicles before public release, and describes the pipeline: data to Connected Bits' servers, likely problems submitted to the city through Open311 and classified as potholes to fix or known obstacles such as speed bumps.",
       "exact": false
      },
      "preservation": {
       "archiveUrl": null,
       "archivedAt": null,
       "contentHash": null,
       "status": "not-captured"
      }
     },
     {
      "id": "src-b949a0b6729e13bf",
      "title": "City of Boston Analytics Team — Automated Pothole Detection (Mercedes-Benz USA pilot, 2023)",
      "url": "https://www.boston.gov/departments/analytics-team/automated-pothole-detection-0",
      "publisher": {
       "name": null,
       "domain": "boston.gov",
       "classification": "not-classified"
      },
      "sourceType": "web-page",
      "language": "und",
      "role": "background",
      "claimRefs": [],
      "temporal": {
       "publishedAt": null,
       "updatedAt": null,
       "sourceCheckedAt": null,
       "recordLastVerifiedAt": "2026-07-26",
       "dossierCreatedAt": "2026-08-22",
       "retrievedAt": null,
       "status": "record-date-only"
      },
      "locator": {
       "type": "descriptive-source-note",
       "value": "What came after, from the same offices. The Analytics Team and New Urban Mechanics tested anonymised Mercedes-Benz vehicle sensor data against 311 reports and found it 'particularly helpful for identifying potential potholes on larger streets with higher speed limits, where 311 reports are relatively rare' — passive sensing used explicitly to cover what the complaint channel misses. It also repeated Street Bump's classification problem: 'other types of irregularities in the road were getting picked up, such as depressions near utility covers.' The page does not mention Street Bump, so no lineage is claimed as fact.",
       "exact": false
      },
      "preservation": {
       "archiveUrl": null,
       "archivedAt": null,
       "contentHash": null,
       "status": "not-captured"
      }
     }
    ],
    "coverage": {
     "claimsTotal": 8,
     "claimsMapped": 8,
     "sourcesTotal": 11,
     "sourcesUsedByClaims": 8,
     "sourcesWithPublicationDate": 7,
     "sourcesWithArchive": 0,
     "sourcesWithExactLocator": 0,
     "sourcesWithContentHash": 0
    },
    "limits": [
     "Publisher names and source languages have not yet been classified; domains are recorded without guessing.",
     "Existing source notes are descriptive locators, not page- or paragraph-exact citations.",
     "A missing archive, publication date or content hash is shown as null rather than inferred.",
     "The project-level evidence grade ranks the strongest source in the entry; it is not a truth score for every claim."
    ]
   }
  }
 ]
}