{
  "id": "AIBD-0333",
  "title": "STF's Victor AI project: OCR text layer in production since December 2020 (10 million+ pages by May 2021); document splitter and theme classifier still pending in August 2021",
  "summary": "STF–UnB Victor: OCR layer live since Dec 2020 (10m+ pages by May 2021); splitter and repercussão geral classifier not yet in production (STF, 19 August 2021); time savings were projections.",
  "date_observed": "2021-08-19",
  "date_basis": "unassessed",
  "date_precision": "unassessed",
  "benefit_status": null,
  "source_collection": null,
  "source_entry": null,
  "account": "Victor is a Supremo Tribunal Federal (STF) and Universidade de Brasília (UnB) partnership. It began at the end of 2017 under Chief Justice Cármen Lúcia. It was designed to read extraordinary appeals and identify the most frequent repercussão geral themes. Its four tasks: converting images to text, splitting documents, classifying the main procedural documents and identifying repercussão geral themes. On 30 August 2018 Cármen Lúcia announced it was in operation, with tests on 27 themes and a claimed triage precision of 84%. She said work taking a clerk three hours would take five seconds. On 23 October 2018 Chief Justice Dias Toffoli spoke at a CNJ-reported seminar. He said it did in five seconds what cost a clerk 40 minutes to an hour, with 85% accuracy. The STF's own status report of 19 August 2021 states that only the optical character recognition (OCR) layer was effectively implemented. It ran from the end of December 2020 for extraordinary appeals and agravos, with over 10 million pages processed by May 2021. The splitter and classifier were still being put into production with no set date. The theme indication is always validated by the ministers. No measured time saving or case outcome was published; the successor tool VitorIA (2023) is recorded separately.",
  "category": "general benefit",
  "unexpected": false,
  "purpose": "Speed up admissibility analysis of extraordinary appeals by flagging the repercussão geral theme that applies. The production OCR layer is a precondition for that, not the classification itself.",
  "system": "Victor (STF–UnB artificial-intelligence project: OCR, document splitter, document classifier, repercussão geral theme classifier)",
  "model_version": null,
  "developer": "Supremo Tribunal Federal (STF) with the Universidade de Brasília (UnB)",
  "deployer": "Supremo Tribunal Federal (STF), Secretaria de Gestão de Precedentes",
  "location": "Brasília, Brazil",
  "domain": "Justice administration: appellate case triage",
  "segment": "Supreme court case management",
  "benefit_type": "Repetitive Task Automation",
  "taxonomy_note": "Only the OCR layer is in production, and the time savings claimed for triage are projections.",
  "registry_ids": {},
  "ratings": [
    {
      "group": "personal",
      "depth": null,
      "reason": "No litigant outcome is documented."
    },
    {
      "group": "organizational",
      "depth": null,
      "reason": "OCR of 10 million pages is a throughput figure, not a measured saving, and the classifier was not in production."
    },
    {
      "group": "societal",
      "depth": null,
      "reason": "No effect on case backlog or litigants is documented."
    }
  ],
  "ai_role": {
    "status": "assessed",
    "statement": "By the STF's own account of 19 August 2021, the AI classifier that would flag repercussão geral themes was not in production. The only live component was automatic OCR of incoming appeals, which the STF frames as part of the Victor project. Any theme indication is validated by the ministers. The time savings announced in 2018 (three hours or 40 minutes to five seconds) were forecasts made by chief justices at launch. All cited sources are the deployer's news service (STF) and the CNJ, which the same chief justice presided. No independent evaluation was found."
  },
  "evidence": [
    {
      "kind": "press release",
      "citation": "Supremo Tribunal Federal (2021). Projeto Victor avança em pesquisa e desenvolvimento para identificação dos temas de repercussão geral. STF Notícias, 19 Aug 2021.",
      "title": "Projeto Victor avança em pesquisa e desenvolvimento para identificação dos temas de repercussão geral",
      "url": "https://noticias.stf.jus.br/postsnoticias/projeto-victor-avanca-em-pesquisa-e-desenvolvimento-para-identificacao-dos-temas-de-repercussao-geral/",
      "date": "2021-08-19",
      "written_by": "makers",
      "quote": "No momento, o STF trabalha para implementar em produção o spliter e o classificador de peças, ainda sem data definida."
    },
    {
      "kind": "press release",
      "citation": "Supremo Tribunal Federal (2021). Same item, section 'Camada de texto dos documentos'.",
      "title": "Projeto Victor avança (OCR layer)",
      "url": "https://noticias.stf.jus.br/postsnoticias/projeto-victor-avanca-em-pesquisa-e-desenvolvimento-para-identificacao-dos-temas-de-repercussao-geral/",
      "date": "2021-08-19",
      "written_by": "makers",
      "quote": "Até maio de 2021, este procedimento ocorreu em mais de 10 milhões de páginas."
    },
    {
      "kind": "press release",
      "citation": "Supremo Tribunal Federal (2018). Ministra Cármen Lúcia anuncia início de funcionamento do Projeto Victor, de inteligência artificial. STF Notícias, 30 Aug 2018.",
      "title": "Ministra Cármen Lúcia anuncia início de funcionamento do Projeto Victor, de inteligência artificial",
      "url": "https://noticias.stf.jus.br/postsnoticias/ministra-carmen-lucia-anuncia-inicio-de-funcionamento-do-projeto-victor-de-inteligencia-artificial/",
      "date": "2018-08-30",
      "written_by": "makers",
      "quote": "Ela informou que atualmente o nível de precisão na triagem é de 84%, mas que atingirá 95% no próximo mês."
    },
    {
      "kind": "report",
      "citation": "Montenegro, M. C. (2018). Inteligência artificial: Trabalho judicial de 40 minutos pode ser feito em 5 segundos. Agência CNJ de Notícias, 23 Oct 2018.",
      "title": "Inteligência artificial: Trabalho judicial de 40 minutos pode ser feito em 5 segundos",
      "url": "https://www.cnj.jus.br/inteligencia-artificial-trabalho-judicial-de-40-minutos-pode-ser-feito-em-5-segundos/",
      "date": "2018-10-23",
      "written_by": "independent",
      "quote": "O trabalho que custaria ao servidor de um tribunal entre 40 minutos e uma hora para fazer, o software faz em cinco segundos."
    },
    {
      "kind": "press release",
      "citation": "Supremo Tribunal Federal (2023). STF finaliza testes de nova ferramenta de Inteligência Artificial. STF news, 11 May 2023.",
      "title": "STF finaliza testes de nova ferramenta de Inteligência Artificial",
      "url": "https://noticias.stf.jus.br/postsnoticias/stf-finaliza-testes-de-nova-ferramenta-de-inteligencia-artificial/",
      "date": "2023-05-11",
      "written_by": "makers",
      "quote": null
    }
  ],
  "replication": {
    "produced_by": [
      "STF–UnB research partnership with about 40 graduate scholarship holders",
      "Building a training set of extraordinary appeals, which took months",
      "Automatic OCR of all incoming appeal PDFs on the day of receipt (from December 2020)",
      "Validation of any theme indication by the ministers"
    ],
    "must_hold": [
      "Filings arriving as PDFs that can be converted to text",
      "Splitter and classifier reaching production (not achieved by August 2021)",
      "Classifier accuracy acceptable to the Secretaria de Gestão de Precedentes",
      "Continued STF–UnB funding and staff"
    ],
    "echoes": [
      {
        "date": "2023-05-17",
        "setting": "STF launched the successor tool VitorIA for grouping and classifying cases (recorded separately as AIBD-0085)",
        "model_version": null,
        "outcome": "unknown",
        "source": "https://noticias.stf.jus.br/postsnoticias/stf-finaliza-testes-de-nova-ferramenta-de-inteligencia-artificial/"
      }
    ]
  },
  "paired_incident": null,
  "headline": "STF's Victor OCR processes 10 million+ pages; classifier pending",
  "metric": "more than 10 million pages processed by May 2021",
  "comparator": {
    "against": "a court clerk converting image files to text by hand",
    "before": "três horas",
    "after": "cinco segundos",
    "basis": "assumed",
    "quote": "A ministra ressaltou que o trabalho de conversão de imagens em texto, por exemplo, que um servidor executa em três horas, será feito em cinco segundos com a nova ferramenta.",
    "url": "https://noticias.stf.jus.br/postsnoticias/ministra-carmen-lucia-anuncia-inicio-de-funcionamento-do-projeto-victor-de-inteligencia-artificial/"
  },
  "later": {
    "status": "persisted",
    "date": "2023-05-11",
    "note": "In May 2023 the STF said it still operated Victor for repercussão geral triage of incoming appeals, alongside Rafa, while finishing tests of the new VitórIA tool.",
    "quote": "Atualmente, o STF opera dois robôs – o Victor, utilizado desde 2017 para análise de temas de repercussão geral na triagem de recursos recebidos de todo país, e a Rafa, desenvolvida para integrar a Agenda 2030 da ONU ao STF, por meio da classificação dos processos de acordo com os Objetivos de Desenvolvimento Sustentável (ODS) definidos pelas Nações Unidas.",
    "url": "https://noticias.stf.jus.br/postsnoticias/stf-finaliza-testes-de-nova-ferramenta-de-inteligencia-artificial/"
  },
  "value": null,
  "developer_country": "Brazil",
  "deployer_country": "Brazil",
  "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": "Added what the benefit is measured against, what happened since from the cited sources.",
      "source": "https://noticias.stf.jus.br/postsnoticias/ministra-carmen-lucia-anuncia-inicio-de-funcionamento-do-projeto-victor-de-inteligencia-artificial/; https://noticias.stf.jus.br/postsnoticias/stf-finaliza-testes-de-nova-ferramenta-de-inteligencia-artificial/",
      "editor": "AIBD"
    },
    {
      "date": "2026-10-21",
      "change": "Benefit type set to Repetitive Task Automation 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": "Country of the developer and the deployer added from the organization's own site or a registry.",
      "source": "https://en.wikipedia.org/wiki/Supreme_Federal_Court; https://en.wikipedia.org/wiki/Supreme_Federal_Court",
      "editor": "AIBD"
    },
    {
      "date": "2026-10-21",
      "change": "Prose pass: account, purpose, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.",
      "source": null,
      "editor": "AIBD"
    }
  ],
  "confidence": "studied",
  "shade": "high",
  "claim_status": "Independently studied",
  "claim_basis": "Sources: one independent report and three press releases from the makers. The cited evidence includes no finding by a regulator or court.",
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        "coverage": "partial",
        "family": "evaluations",
        "display": {
          "answer": "more than 10 million pages processed by May 2021",
          "editor_note": ""
        }
      },
      "evaluations[].reproduction_materials": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial",
        "family": "evaluations"
      },
      "evaluations[].setting_sample": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial",
        "family": "evaluations"
      },
      "evaluations[].uncertainty_limitations": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial",
        "family": "evaluations"
      },
      "external_references[]": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial"
      },
      "follow_up[]": {
        "state": "value_present",
        "value": {
          "kind": "record_fields",
          "fields": {
            "later": {
              "status": "persisted",
              "date": "2023-05-11",
              "note": "In May 2023 the STF said it still operated Victor for repercussão geral triage of incoming appeals, alongside Rafa, while finishing tests of the new VitórIA tool.",
              "quote": "Atualmente, o STF opera dois robôs – o Victor, utilizado desde 2017 para análise de temas de repercussão geral na triagem de recursos recebidos de todo país, e a Rafa, desenvolvida para integrar a Agenda 2030 da ONU ao STF, por meio da classificação dos processos de acordo com os Objetivos de Desenvolvimento Sustentável (ODS) definidos pelas Nações Unidas.",
              "url": "https://noticias.stf.jus.br/postsnoticias/stf-finaliza-testes-de-nova-ferramenta-de-inteligencia-artificial/"
            }
          }
        },
        "provenance": {
          "kind": "legacy_record",
          "basis": "Preserved from the existing AIBD record; external sources were not rechecked.",
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            "later"
          ],
          "source_refs": []
        },
        "review_state": "record_mapped",
        "coverage": "partial"
      },
      "human_oversight": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial"
      },
      "id": {
        "state": "value_present",
        "value": {
          "kind": "record_fields",
          "fields": {
            "id": "AIBD-0333"
          }
        },
        "provenance": {
          "kind": "legacy_record",
          "basis": "Preserved from the existing AIBD record; external sources were not rechecked.",
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            "id"
          ],
          "source_refs": []
        },
        "review_state": "record_mapped",
        "coverage": "partial"
      },
      "infrastructure_dependencies[]": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial"
      },
      "outcomes[].beneficiaries": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial"
      },
      "outcomes[].beneficiary_count": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial"
      },
      "outcomes[].benefit_type": {
        "state": "value_present",
        "value": {
          "kind": "record_fields",
          "fields": {
            "benefit_type": "Repetitive Task Automation",
            "taxonomy_note": "Only the OCR layer is in production, and the time savings claimed for triage are projections."
          }
        },
        "provenance": {
          "kind": "legacy_record",
          "basis": "Preserved from the existing AIBD record; external sources were not rechecked.",
          "record_fields": [
            "benefit_type",
            "taxonomy_note"
          ],
          "source_refs": []
        },
        "review_state": "record_mapped",
        "coverage": "partial"
      },
      "outcomes[].claim": {
        "state": "value_present",
        "value": {
          "kind": "record_fields",
          "fields": {
            "account": "Victor is a Supremo Tribunal Federal (STF) and Universidade de Brasília (UnB) partnership. It began at the end of 2017 under Chief Justice Cármen Lúcia. It was designed to read extraordinary appeals and identify the most frequent repercussão geral themes. Its four tasks: converting images to text, splitting documents, classifying the main procedural documents and identifying repercussão geral themes. On 30 August 2018 Cármen Lúcia announced it was in operation, with tests on 27 themes and a claimed triage precision of 84%. She said work taking a clerk three hours would take five seconds. On 23 October 2018 Chief Justice Dias Toffoli spoke at a CNJ-reported seminar. He said it did in five seconds what cost a clerk 40 minutes to an hour, with 85% accuracy. The STF's own status report of 19 August 2021 states that only the optical character recognition (OCR) layer was effectively implemented. It ran from the end of December 2020 for extraordinary appeals and agravos, with over 10 million pages processed by May 2021. The splitter and classifier were still being put into production with no set date. The theme indication is always validated by the ministers. No measured time saving or case outcome was published; the successor tool VitorIA (2023) is recorded separately.",
            "benefit_type": "Repetitive Task Automation"
          }
        },
        "provenance": {
          "kind": "legacy_record",
          "basis": "Preserved from the existing AIBD record; external sources were not rechecked.",
          "record_fields": [
            "account",
            "benefit_type"
          ],
          "source_refs": []
        },
        "review_state": "record_mapped",
        "coverage": "partial"
      },
      "outcomes[].distribution": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial"
      },
      "outcomes[].duration": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial"
      },
      "outcomes[].id": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial"
      },
      "outcomes[].stakeholder_group": {
        "state": "value_present",
        "value": {
          "kind": "record_fields",
          "fields": {
            "ratings": [
              {
                "group": "personal",
                "reason": "No litigant outcome is documented."
              },
              {
                "group": "organizational",
                "reason": "OCR of 10 million pages is a throughput figure, not a measured saving, and the classifier was not in production."
              },
              {
                "group": "societal",
                "reason": "No effect on case backlog or litigants is documented."
              }
            ]
          }
        },
        "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": "Speed up admissibility analysis of extraordinary appeals by flagging the repercussão geral theme that applies. The production OCR layer is a precondition for that, not the classification itself."
          }
        },
        "provenance": {
          "kind": "legacy_record",
          "basis": "Preserved from the existing AIBD record; external sources were not rechecked.",
          "record_fields": [
            "unexpected",
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          ],
          "source_refs": []
        },
        "review_state": "record_mapped",
        "coverage": "partial"
      },
      "places[]": {
        "state": "value_present",
        "value": {
          "kind": "record_fields",
          "fields": {
            "location": "Brasília, Brazil",
            "developer_country": "Brazil",
            "deployer_country": "Brazil"
          }
        },
        "provenance": {
          "kind": "legacy_record",
          "basis": "Preserved from the existing AIBD record; external sources were not rechecked.",
          "record_fields": [
            "location",
            "developer_country",
            "deployer_country"
          ],
          "source_refs": []
        },
        "review_state": "record_mapped",
        "coverage": "partial"
      },
      "purpose": {
        "state": "value_present",
        "value": {
          "kind": "record_fields",
          "fields": {
            "purpose": "Speed up admissibility analysis of extraordinary appeals by flagging the repercussão geral theme that applies. The production OCR layer is a precondition for that, not the classification itself."
          }
        },
        "provenance": {
          "kind": "legacy_record",
          "basis": "Preserved from the existing AIBD record; external sources were not rechecked.",
          "record_fields": [
            "purpose"
          ],
          "source_refs": []
        },
        "review_state": "record_mapped",
        "coverage": "partial"
      },
      "record_dates": {
        "state": "value_present",
        "value": {
          "kind": "record_fields",
          "fields": {
            "date_observed": "2021-08-19",
            "revisions": [
              {
                "date": "2026-10-20",
                "change": "Record drafted from the cited sources.",
                "editor": "AIBD"
              },
              {
                "date": "2026-10-21",
                "change": "Added what the benefit is measured against, what happened since from the cited sources.",
                "source": "https://noticias.stf.jus.br/postsnoticias/ministra-carmen-lucia-anuncia-inicio-de-funcionamento-do-projeto-victor-de-inteligencia-artificial/; https://noticias.stf.jus.br/postsnoticias/stf-finaliza-testes-de-nova-ferramenta-de-inteligencia-artificial/",
                "editor": "AIBD"
              },
              {
                "date": "2026-10-21",
                "change": "Benefit type set to Repetitive Task Automation 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": "Country of the developer and the deployer added from the organization's own site or a registry.",
                "source": "https://en.wikipedia.org/wiki/Supreme_Federal_Court; https://en.wikipedia.org/wiki/Supreme_Federal_Court",
                "editor": "AIBD"
              },
              {
                "date": "2026-10-21",
                "change": "Prose pass: account, purpose, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.",
                "editor": "AIBD"
              }
            ]
          }
        },
        "provenance": {
          "kind": "legacy_record",
          "basis": "Preserved from the existing AIBD record; external sources were not rechecked.",
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          ],
          "source_refs": []
        },
        "review_state": "record_mapped",
        "coverage": "partial"
      },
      "replication.attempts[]": {
        "state": "value_present",
        "value": {
          "kind": "record_fields",
          "fields": {
            "replication": {
              "produced_by": [
                "STF–UnB research partnership with about 40 graduate scholarship holders",
                "Building a training set of extraordinary appeals, which took months",
                "Automatic OCR of all incoming appeal PDFs on the day of receipt (from December 2020)",
                "Validation of any theme indication by the ministers"
              ],
              "must_hold": [
                "Filings arriving as PDFs that can be converted to text",
                "Splitter and classifier reaching production (not achieved by August 2021)",
                "Classifier accuracy acceptable to the Secretaria de Gestão de Precedentes",
                "Continued STF–UnB funding and staff"
              ],
              "echoes": [
                {
                  "date": "2023-05-17",
                  "setting": "STF launched the successor tool VitorIA for grouping and classifying cases (recorded separately as AIBD-0085)",
                  "outcome": "unknown",
                  "source": "https://noticias.stf.jus.br/postsnoticias/stf-finaliza-testes-de-nova-ferramenta-de-inteligencia-artificial/"
                }
              ]
            }
          }
        },
        "provenance": {
          "kind": "legacy_record",
          "basis": "Preserved from the existing AIBD record; external sources were not rechecked.",
          "record_fields": [
            "replication"
          ],
          "source_refs": []
        },
        "review_state": "record_mapped",
        "coverage": "partial"
      },
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        "value": {
          "kind": "record_fields",
          "fields": {
            "replication": {
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                "Validation of any theme indication by the ministers"
              ],
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                "Filings arriving as PDFs that can be converted to text",
                "Splitter and classifier reaching production (not achieved by August 2021)",
                "Classifier accuracy acceptable to the Secretaria de Gestão de Precedentes",
                "Continued STF–UnB funding and staff"
              ],
              "echoes": [
                {
                  "date": "2023-05-17",
                  "setting": "STF launched the successor tool VitorIA for grouping and classifying cases (recorded separately as AIBD-0085)",
                  "outcome": "unknown",
                  "source": "https://noticias.stf.jus.br/postsnoticias/stf-finaliza-testes-de-nova-ferramenta-de-inteligencia-artificial/"
                }
              ]
            }
          }
        },
        "provenance": {
          "kind": "legacy_record",
          "basis": "Preserved from the existing AIBD record; external sources were not rechecked.",
          "record_fields": [
            "replication"
          ],
          "source_refs": []
        },
        "review_state": "record_mapped",
        "coverage": "partial"
      },
      "revision_history[]": {
        "state": "value_present",
        "value": {
          "kind": "record_fields",
          "fields": {
            "revisions": [
              {
                "date": "2026-10-20",
                "change": "Record drafted from the cited sources.",
                "editor": "AIBD"
              },
              {
                "date": "2026-10-21",
                "change": "Added what the benefit is measured against, what happened since from the cited sources.",
                "source": "https://noticias.stf.jus.br/postsnoticias/ministra-carmen-lucia-anuncia-inicio-de-funcionamento-do-projeto-victor-de-inteligencia-artificial/; https://noticias.stf.jus.br/postsnoticias/stf-finaliza-testes-de-nova-ferramenta-de-inteligencia-artificial/",
                "editor": "AIBD"
              },
              {
                "date": "2026-10-21",
                "change": "Benefit type set to Repetitive Task Automation 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": "Country of the developer and the deployer added from the organization's own site or a registry.",
                "source": "https://en.wikipedia.org/wiki/Supreme_Federal_Court; https://en.wikipedia.org/wiki/Supreme_Federal_Court",
                "editor": "AIBD"
              },
              {
                "date": "2026-10-21",
                "change": "Prose pass: account, purpose, the replication conditions, 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"
      },
      "sector": {
        "state": "value_present",
        "value": {
          "kind": "record_fields",
          "fields": {
            "domain": "Justice administration: appellate case triage",
            "segment": "Supreme court case management"
          }
        },
        "provenance": {
          "kind": "legacy_record",
          "basis": "Preserved from the existing AIBD record; external sources were not rechecked.",
          "record_fields": [
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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": "press release",
                "citation": "Supremo Tribunal Federal (2021). Projeto Victor avança em pesquisa e desenvolvimento para identificação dos temas de repercussão geral. STF Notícias, 19 Aug 2021.",
                "title": "Projeto Victor avança em pesquisa e desenvolvimento para identificação dos temas de repercussão geral",
                "url": "https://noticias.stf.jus.br/postsnoticias/projeto-victor-avanca-em-pesquisa-e-desenvolvimento-para-identificacao-dos-temas-de-repercussao-geral/",
                "date": "2021-08-19",
                "written_by": "makers",
                "quote": "No momento, o STF trabalha para implementar em produção o spliter e o classificador de peças, ainda sem data definida."
              },
              {
                "kind": "press release",
                "citation": "Supremo Tribunal Federal (2021). Same item, section 'Camada de texto dos documentos'.",
                "title": "Projeto Victor avança (OCR layer)",
                "url": "https://noticias.stf.jus.br/postsnoticias/projeto-victor-avanca-em-pesquisa-e-desenvolvimento-para-identificacao-dos-temas-de-repercussao-geral/",
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                "written_by": "makers",
                "quote": "Até maio de 2021, este procedimento ocorreu em mais de 10 milhões de páginas."
              },
              {
                "kind": "press release",
                "citation": "Supremo Tribunal Federal (2018). Ministra Cármen Lúcia anuncia início de funcionamento do Projeto Victor, de inteligência artificial. STF Notícias, 30 Aug 2018.",
                "title": "Ministra Cármen Lúcia anuncia início de funcionamento do Projeto Victor, de inteligência artificial",
                "url": "https://noticias.stf.jus.br/postsnoticias/ministra-carmen-lucia-anuncia-inicio-de-funcionamento-do-projeto-victor-de-inteligencia-artificial/",
                "date": "2018-08-30",
                "written_by": "makers",
                "quote": "Ela informou que atualmente o nível de precisão na triagem é de 84%, mas que atingirá 95% no próximo mês."
              },
              {
                "kind": "report",
                "citation": "Montenegro, M. C. (2018). Inteligência artificial: Trabalho judicial de 40 minutos pode ser feito em 5 segundos. Agência CNJ de Notícias, 23 Oct 2018.",
                "title": "Inteligência artificial: Trabalho judicial de 40 minutos pode ser feito em 5 segundos",
                "url": "https://www.cnj.jus.br/inteligencia-artificial-trabalho-judicial-de-40-minutos-pode-ser-feito-em-5-segundos/",
                "date": "2018-10-23",
                "written_by": "independent",
                "quote": "O trabalho que custaria ao servidor de um tribunal entre 40 minutos e uma hora para fazer, o software faz em cinco segundos."
              },
              {
                "kind": "press release",
                "citation": "Supremo Tribunal Federal (2023). STF finaliza testes de nova ferramenta de Inteligência Artificial. STF news, 11 May 2023.",
                "title": "STF finaliza testes de nova ferramenta de Inteligência Artificial",
                "url": "https://noticias.stf.jus.br/postsnoticias/stf-finaliza-testes-de-nova-ferramenta-de-inteligencia-artificial/",
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                "written_by": "makers"
              }
            ]
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        },
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      },
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            "AIBD-0333-source-0003",
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            "AIBD-0333-source-0005"
          ],
          "role": "Existing evidence items, in their original order"
        },
        "provenance": {
          "kind": "curatorial",
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        },
        "review_state": "record_mapped",
        "coverage": "complete"
      },
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          "kind": "record_fields",
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                "kind": "press release",
                "citation": "Supremo Tribunal Federal (2021). Projeto Victor avança em pesquisa e desenvolvimento para identificação dos temas de repercussão geral. STF Notícias, 19 Aug 2021.",
                "title": "Projeto Victor avança em pesquisa e desenvolvimento para identificação dos temas de repercussão geral",
                "url": "https://noticias.stf.jus.br/postsnoticias/projeto-victor-avanca-em-pesquisa-e-desenvolvimento-para-identificacao-dos-temas-de-repercussao-geral/",
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                "url": "https://noticias.stf.jus.br/postsnoticias/projeto-victor-avanca-em-pesquisa-e-desenvolvimento-para-identificacao-dos-temas-de-repercussao-geral/",
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              },
              {
                "kind": "press release",
                "citation": "Supremo Tribunal Federal (2018). Ministra Cármen Lúcia anuncia início de funcionamento do Projeto Victor, de inteligência artificial. STF Notícias, 30 Aug 2018.",
                "title": "Ministra Cármen Lúcia anuncia início de funcionamento do Projeto Victor, de inteligência artificial",
                "url": "https://noticias.stf.jus.br/postsnoticias/ministra-carmen-lucia-anuncia-inicio-de-funcionamento-do-projeto-victor-de-inteligencia-artificial/",
                "date": "2018-08-30",
                "written_by": "makers",
                "quote": "Ela informou que atualmente o nível de precisão na triagem é de 84%, mas que atingirá 95% no próximo mês."
              },
              {
                "kind": "report",
                "citation": "Montenegro, M. C. (2018). Inteligência artificial: Trabalho judicial de 40 minutos pode ser feito em 5 segundos. Agência CNJ de Notícias, 23 Oct 2018.",
                "title": "Inteligência artificial: Trabalho judicial de 40 minutos pode ser feito em 5 segundos",
                "url": "https://www.cnj.jus.br/inteligencia-artificial-trabalho-judicial-de-40-minutos-pode-ser-feito-em-5-segundos/",
                "date": "2018-10-23",
                "written_by": "independent",
                "quote": "O trabalho que custaria ao servidor de um tribunal entre 40 minutos e uma hora para fazer, o software faz em cinco segundos."
              },
              {
                "kind": "press release",
                "citation": "Supremo Tribunal Federal (2023). STF finaliza testes de nova ferramenta de Inteligência Artificial. STF news, 11 May 2023.",
                "title": "STF finaliza testes de nova ferramenta de Inteligência Artificial",
                "url": "https://noticias.stf.jus.br/postsnoticias/stf-finaliza-testes-de-nova-ferramenta-de-inteligencia-artificial/",
                "date": "2023-05-11",
                "written_by": "makers"
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        },
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          "kind": "legacy_record",
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            "evidence"
          ],
          "source_refs": []
        },
        "review_state": "record_mapped",
        "coverage": "partial"
      },
      "sources[].upstream_sources": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial"
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        "state": "not_assessed",
        "review_state": "not_assessed",
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        "family": "strategic assessments"
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      "summary": {
        "state": "value_present",
        "value": {
          "kind": "record_fields",
          "fields": {
            "summary": "STF–UnB Victor: OCR layer live since Dec 2020 (10m+ pages by May 2021); splitter and repercussão geral classifier not yet in production (STF, 19 August 2021); time savings were projections.",
            "account": "Victor is a Supremo Tribunal Federal (STF) and Universidade de Brasília (UnB) partnership. It began at the end of 2017 under Chief Justice Cármen Lúcia. It was designed to read extraordinary appeals and identify the most frequent repercussão geral themes. Its four tasks: converting images to text, splitting documents, classifying the main procedural documents and identifying repercussão geral themes. On 30 August 2018 Cármen Lúcia announced it was in operation, with tests on 27 themes and a claimed triage precision of 84%. She said work taking a clerk three hours would take five seconds. On 23 October 2018 Chief Justice Dias Toffoli spoke at a CNJ-reported seminar. He said it did in five seconds what cost a clerk 40 minutes to an hour, with 85% accuracy. The STF's own status report of 19 August 2021 states that only the optical character recognition (OCR) layer was effectively implemented. It ran from the end of December 2020 for extraordinary appeals and agravos, with over 10 million pages processed by May 2021. The splitter and classifier were still being put into production with no set date. The theme indication is always validated by the ministers. No measured time saving or case outcome was published; the successor tool VitorIA (2023) is recorded separately."
          }
        },
        "provenance": {
          "kind": "legacy_record",
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            "account"
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          "source_refs": []
        },
        "review_state": "record_mapped",
        "coverage": "partial"
      },
      "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"
      },
      "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": "Victor (STF–UnB artificial-intelligence project: OCR, document splitter, document classifier, repercussão geral theme classifier)"
          }
        },
        "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": "Supremo Tribunal Federal (STF) with the Universidade de Brasília (UnB)"
          }
        },
        "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": "STF's Victor AI project: OCR text layer in production since December 2020 (10 million+ pages by May 2021); document splitter and theme classifier still pending in August 2021",
            "headline": "STF's Victor OCR processes 10 million+ pages; classifier pending"
          }
        },
        "provenance": {
          "kind": "legacy_record",
          "basis": "Preserved from the existing AIBD record; external sources were not rechecked.",
          "record_fields": [
            "title",
            "headline"
          ],
          "source_refs": []
        },
        "review_state": "record_mapped",
        "coverage": "partial"
      },
      "version": {
        "state": "value_present",
        "value": {
          "kind": "identifiers",
          "identifiers": [
            "4d5f8f71116fb7bb19b2d4484b2b91fcb96dc1b3e49c846c55f7fae0dffbeab5"
          ],
          "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": "Victor is a Supremo Tribunal Federal (STF) and Universidade de Brasília (UnB) partnership. It began at the end of 2017 under Chief Justice Cármen Lúcia. It was designed to read extraordinary appeals and identify the most frequent repercussão geral themes. Its four tasks: converting images to text, splitting documents, classifying the main procedural documents and identifying repercussão geral themes. On 30 August 2018 Cármen Lúcia announced it was in operation, with tests on 27 themes and a claimed triage precision of 84%. She said work taking a clerk three hours would take five seconds. On 23 October 2018 Chief Justice Dias Toffoli spoke at a CNJ-reported seminar. He said it did in five seconds what cost a clerk 40 minutes to an hour, with 85% accuracy. The STF's own status report of 19 August 2021 states that only the optical character recognition (OCR) layer was effectively implemented. It ran from the end of December 2020 for extraordinary appeals and agravos, with over 10 million pages processed by May 2021. The splitter and classifier were still being put into production with no set date. The theme indication is always validated by the ministers. No measured time saving or case outcome was published; the successor tool VitorIA (2023) is recorded separately.",
            "replication": {
              "produced_by": [
                "STF–UnB research partnership with about 40 graduate scholarship holders",
                "Building a training set of extraordinary appeals, which took months",
                "Automatic OCR of all incoming appeal PDFs on the day of receipt (from December 2020)",
                "Validation of any theme indication by the ministers"
              ],
              "must_hold": [
                "Filings arriving as PDFs that can be converted to text",
                "Splitter and classifier reaching production (not achieved by August 2021)",
                "Classifier accuracy acceptable to the Secretaria de Gestão de Precedentes",
                "Continued STF–UnB funding and staff"
              ],
              "echoes": [
                {
                  "date": "2023-05-17",
                  "setting": "STF launched the successor tool VitorIA for grouping and classifying cases (recorded separately as AIBD-0085)",
                  "outcome": "unknown",
                  "source": "https://noticias.stf.jus.br/postsnoticias/stf-finaliza-testes-de-nova-ferramenta-de-inteligencia-artificial/"
                }
              ]
            }
          }
        },
        "provenance": {
          "kind": "legacy_record",
          "basis": "Preserved from the existing AIBD record; external sources were not rechecked.",
          "record_fields": [
            "account",
            "replication"
          ],
          "source_refs": []
        },
        "review_state": "record_mapped",
        "coverage": "partial"
      }
    },
    "summary": {
      "total": 91,
      "addressed": 91,
      "known": 28,
      "reviewed": 0,
      "record_mapped": 28,
      "not_assessed": 63,
      "complete": false,
      "by_state": {
        "value_present": 28,
        "not_reported": 0,
        "unresolved": 0,
        "not_assessed": 63,
        "not_applicable": 0,
        "withheld": 0,
        "source_unavailable": 0
      }
    }
  }
}