{
  "id": "AIBD-0017",
  "title": "Ithaca: deep neural network for restoring, placing and dating ancient Greek inscriptions (capability study)",
  "summary": "Ithaca restores and dates Greek inscriptions with measured accuracy gains; no specific accepted restoration verified.",
  "date_observed": "2022-03-09",
  "date_basis": "unassessed",
  "date_precision": "unassessed",
  "benefit_status": null,
  "source_collection": null,
  "source_entry": null,
  "account": "On 9 March 2022 Nature published Ithaca, a deep neural network built by DeepMind with the Universities of Venice Ca' Foscari, Oxford and Athens. Authors include Assael, Sommerschield, Prag and de Freitas. It restores missing text in damaged ancient Greek inscriptions and attributes them to place and date. Ithaca restored text with 62 percent accuracy alone and raised historians' accuracy from 25 to 72 percent when they worked with it. It attributed inscriptions geographically with 71 percent accuracy and dated them to within about 30 years. Its average date of 421 BC for a group of disputed Athenian decrees agreed with recent independent re-dating. The cited sources report accuracy studies and a public interface, not a specific inscription whose restoration epigraphers have since accepted.",
  "category": "general benefit",
  "unexpected": false,
  "purpose": "Built to restore, geographically attribute and date ancient Greek inscriptions; the reported capability was that purpose.",
  "system": "Ithaca (transformer-based deep neural network for epigraphic restoration and attribution)",
  "model_version": "Ithaca (2022)",
  "developer": "DeepMind with Ca' Foscari University of Venice, University of Oxford and Athens University of Economics and Business",
  "deployer": "Research consortium; public interface at ithaca.deepmind.com",
  "location": null,
  "domain": "Humanities / epigraphy",
  "segment": "Academic research",
  "benefit_type": "Improved Task Accuracy",
  "taxonomy_note": null,
  "registry_ids": {},
  "ratings": [
    {
      "group": "societal",
      "depth": null,
      "reason": "The sources give accuracy measurements and a prediction that agrees with independent re-dating of Athenian decrees. No discrete, verified downstream result is attributable to Ithaca."
    }
  ],
  "ai_role": {
    "status": "assessed",
    "statement": "Ithaca's outputs were evaluated against held-out ground truth and in a study of historians working with and without it. The 421 BC dating is a model prediction consistent with evidence established by other means, not an outcome the model caused."
  },
  "evidence": [
    {
      "kind": "peer-reviewed study",
      "citation": "Assael, Y., Sommerschield, T. et al. (2022). Restoring and attributing ancient texts using deep neural networks. Nature 603, 280–283.",
      "title": "Restoring and attributing ancient texts using deep neural networks",
      "url": "https://www.nature.com/articles/s41586-022-04448-z",
      "date": "2022-03-09",
      "written_by": "makers",
      "quote": "Ithaca's average predicted date for all of these decrees is 421 bc"
    },
    {
      "kind": "press release",
      "citation": "Ca' Foscari University of Venice (2022). AI to aid historians in restoring, placing and dating ancient texts. University news.",
      "title": "AI to aid historians in restoring, placing and dating ancient texts",
      "url": "https://www.unive.it/pag/16584/?tx_news_pi1%5Bnews%5D=12037",
      "date": "2022-03-09",
      "written_by": "makers",
      "quote": "have already used the model to shed light on current disputes in Greek history"
    },
    {
      "kind": "report",
      "citation": "Roueché, C. (2022). AI minds the gap and fills in missing Greek inscriptions. Nature, News & Views, 9 March 2022. doi:10.1038/d41586-022-00641-2",
      "title": "AI minds the gap and fills in missing Greek inscriptions",
      "url": "https://www.nature.com/articles/d41586-022-00641-2",
      "date": "2022-03-09",
      "written_by": "independent",
      "quote": "A new AI tool helps to fill in missing text and estimate the timeframe and geographical origin of ancient inscriptions."
    },
    {
      "kind": "news report",
      "citation": "Google DeepMind's new AI can help historians understand ancient Latin inscriptions. MIT Technology Review, 23 July 2025.",
      "title": "Google DeepMind's new AI can help historians understand ancient Latin inscriptions",
      "url": "https://www.technologyreview.com/2025/07/23/1120574/deepmind-ai-aeneas-helps-historians-interpret-latin-inscriptions/",
      "date": "2025-07-23",
      "written_by": "independent",
      "quote": null
    }
  ],
  "replication": {
    "produced_by": [
      "A large digitized corpus of inscriptions with known provenance and date",
      "A neural network trained on artificially damaged text",
      "Evaluation against held-out inscriptions and with historians in the loop"
    ],
    "must_hold": [
      "A verified event of a benefit created or restored would require an epigrapher-accepted restoration or re-dating attributable to the model and documented in a non-vendor source"
    ],
    "echoes": []
  },
  "paired_incident": null,
  "headline": "Ithaca raises historians' accuracy from 25 to 72 percent",
  "metric": "raised historians' accuracy from 25 to 72 percent when they worked with it",
  "comparator": {
    "against": "historians restoring damaged texts without the tool",
    "before": "25%",
    "after": "72% (historians using Ithaca); 62% for Ithaca alone",
    "basis": "measured",
    "quote": "While Ithaca alone achieves 62% accuracy when restoring damaged texts, the use of Ithaca by historians improved their accuracy from 25% to 72%, confirming the synergistic effect of this research tool.",
    "url": "https://europepmc.org/article/PMC/PMC8907065"
  },
  "later": {
    "status": "scaled",
    "date": "2025-07-23",
    "note": "DeepMind followed Ithaca with Aeneas, a successor model for Latin inscriptions published in Nature on 23 July 2025, extending the approach to a second language and to retrieval of parallel texts.",
    "quote": "It follows Google's previous archaeological tool Ithaca, which also used deep learning to reconstruct and contextualize ancient text, in its case Greek. But while Ithaca and Aeneas use some similar systems, Aeneas also promises to give researchers jumping-off points for further analysis.",
    "url": "https://www.technologyreview.com/2025/07/23/1120574/deepmind-ai-aeneas-helps-historians-interpret-latin-inscriptions/"
  },
  "value": null,
  "developer_country": "United Kingdom",
  "deployer_country": null,
  "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": "2026-10-20",
      "change": "Verification sweep: both sources were written by Ithaca's developers (DeepMind and Ca' Foscari), so the Ca' Foscari page is now marked vendor-authored and a Nature News & Views by Charlotte Roueché, who is not an author of the paper, is added as the independent source.",
      "source": "https://www.nature.com/articles/d41586-022-00641-2",
      "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 Knowledge Discovery 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://europepmc.org/article/PMC/PMC8907065; https://www.technologyreview.com/2025/07/23/1120574/deepmind-ai-aeneas-helps-historians-interpret-latin-inscriptions/",
      "editor": "AIBD"
    },
    {
      "date": "2026-10-21",
      "change": "Country of the developer added from the organization's own site or a registry.",
      "source": "https://en.wikipedia.org/wiki/Google_DeepMind",
      "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"
    }
  ],
  "confidence": "studied",
  "shade": "high",
  "claim_status": "Independently studied",
  "claim_basis": "Sources: one independent report, one independent news report, the makers' peer-reviewed study and the makers' press release. The cited evidence includes no finding by a regulator or court.",
  "profile": {
    "case_id": "AIBD-0017",
    "catalog_version": "1.0.0",
    "source_fingerprint": "302890ae1f7d56f3ea61c181dd07fed38ed62453610ee0492cdfece78ec510bf",
    "collection_refs": [
      {
        "collection_id": "aibd",
        "version": "snapshot-8bf7cfec9cb185aa"
      },
      {
        "collection_id": "aibd",
        "version": "2026-10-25"
      }
    ],
    "source_ids": [
      "AIBD-0017-source-0001",
      "AIBD-0017-source-0002",
      "AIBD-0017-source-0003",
      "AIBD-0017-source-0004"
    ],
    "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": "DeepMind with Ca' Foscari University of Venice, University of Oxford and Athens University of Economics and Business",
            "deployer": "Research consortium; public interface at ithaca.deepmind.com"
          }
        },
        "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": "Ithaca's outputs were evaluated against held-out ground truth and in a study of historians working with and without it. The 421 BC dating is a model prediction consistent with evidence established by other means, not an outcome the model caused."
            }
          }
        },
        "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": "general benefit",
            "ai_role": {
              "status": "assessed",
              "statement": "Ithaca's outputs were evaluated against held-out ground truth and in a study of historians working with and without it. The 421 BC dating is a model prediction consistent with evidence established by other means, not an outcome the model caused."
            }
          }
        },
        "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": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial"
      },
      "assessments[].display_confidence": {
        "state": "value_present",
        "value": {
          "kind": "structured",
          "data": {
            "confidence": "high",
            "evidence_word": "studied",
            "claim_status": "Independently studied",
            "basis": "Sources: one independent report, one independent news report, the makers' peer-reviewed study and the makers' press release. The cited evidence includes no finding by a regulator or court.",
            "interpretation": "Existing display rule preserved; the richer outcome-specific confidence question still needs review."
          }
        },
        "provenance": {
          "kind": "curatorial",
          "basis": "Derived from the existing case display properties; not a new evidentiary judgment.",
          "record_fields": [],
          "source_refs": []
        },
        "review_state": "record_mapped",
        "coverage": "partial"
      },
      "assessments[].rationale": {
        "state": "value_present",
        "value": {
          "kind": "record_fields",
          "fields": {
            "ratings": [
              {
                "group": "societal",
                "reason": "The sources give accuracy measurements and a prediction that agrees with independent re-dating of Athenian decrees. No discrete, verified downstream result is attributable to Ithaca."
              }
            ]
          }
        },
        "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"
      },
      "assessments[].rubric_version": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial"
      },
      "assessments[].status_basis": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial"
      },
      "assessments[].target": {
        "state": "value_present",
        "value": {
          "kind": "record_fields",
          "fields": {
            "ratings": [
              {
                "group": "societal",
                "reason": "The sources give accuracy measurements and a prediction that agrees with independent re-dating of Athenian decrees. No discrete, verified downstream result is attributable to Ithaca."
              }
            ]
          }
        },
        "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"
      },
      "availability": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial"
      },
      "brief": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial"
      },
      "case_kind": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial"
      },
      "case_links[]": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial"
      },
      "claims[].evidence_links": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial"
      },
      "claims[].evidence_strength": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial"
      },
      "claims[].missingness": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial"
      },
      "claims[].proposition": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial"
      },
      "cross_border_flows[]": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial"
      },
      "decisions[].action_outcome": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial",
        "family": "institutional decisions"
      },
      "decisions[].grounds": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial",
        "family": "institutional decisions"
      },
      "decisions[].institution_task": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial",
        "family": "institutional decisions"
      },
      "decisions[].success_criterion": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial",
        "family": "institutional decisions"
      },
      "deployment_stage": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial"
      },
      "economics[].amount": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial",
        "family": "monetary observations"
      },
      "economics[].costs[]": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial",
        "family": "monetary observations"
      },
      "economics[].counterfactual": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial",
        "family": "monetary observations"
      },
      "economics[].currency_price_basis": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial",
        "family": "monetary observations"
      },
      "economics[].gross_net": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial",
        "family": "monetary observations"
      },
      "economics[].measure_type": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial",
        "family": "monetary observations"
      },
      "economics[].period_unit": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial",
        "family": "monetary observations"
      },
      "economics[].realization": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial",
        "family": "monetary observations"
      },
      "economics[].recipient_payer": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial",
        "family": "monetary observations"
      },
      "economics[].return_payback": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial",
        "family": "monetary observations"
      },
      "economics[].valuation_method": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial",
        "family": "monetary observations"
      },
      "evaluations[].ai_uplift": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial",
        "family": "evaluations"
      },
      "evaluations[].comparator": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial",
        "family": "evaluations"
      },
      "evaluations[].design": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial",
        "family": "evaluations"
      },
      "evaluations[].effect": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial",
        "family": "evaluations"
      },
      "evaluations[].exposure": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial",
        "family": "evaluations"
      },
      "evaluations[].id": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial",
        "family": "evaluations"
      },
      "evaluations[].measurement_method": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial",
        "family": "evaluations"
      },
      "evaluations[].metric": {
        "state": "not_assessed",
        "provenance": {
          "kind": "legacy_record",
          "basis": "Mapped from the existing case description without a new external source review.",
          "record_fields": [
            "metric"
          ],
          "source_refs": []
        },
        "review_state": "not_assessed",
        "coverage": "partial",
        "family": "evaluations",
        "display": {
          "answer": "raised historians' accuracy from 25 to 72 percent when they worked with it",
          "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": "scaled",
              "date": "2025-07-23",
              "note": "DeepMind followed Ithaca with Aeneas, a successor model for Latin inscriptions published in Nature on 23 July 2025, extending the approach to a second language and to retrieval of parallel texts.",
              "quote": "It follows Google's previous archaeological tool Ithaca, which also used deep learning to reconstruct and contextualize ancient text, in its case Greek. But while Ithaca and Aeneas use some similar systems, Aeneas also promises to give researchers jumping-off points for further analysis.",
              "url": "https://www.technologyreview.com/2025/07/23/1120574/deepmind-ai-aeneas-helps-historians-interpret-latin-inscriptions/"
            }
          }
        },
        "provenance": {
          "kind": "legacy_record",
          "basis": "Preserved from the existing AIBD record; external sources were not rechecked.",
          "record_fields": [
            "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-0017"
          }
        },
        "provenance": {
          "kind": "legacy_record",
          "basis": "Preserved from the existing AIBD record; external sources were not rechecked.",
          "record_fields": [
            "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": "Improved Task Accuracy"
          }
        },
        "provenance": {
          "kind": "legacy_record",
          "basis": "Preserved from the existing AIBD record; external sources were not rechecked.",
          "record_fields": [
            "benefit_type"
          ],
          "source_refs": []
        },
        "review_state": "record_mapped",
        "coverage": "partial"
      },
      "outcomes[].claim": {
        "state": "value_present",
        "value": {
          "kind": "record_fields",
          "fields": {
            "account": "On 9 March 2022 Nature published Ithaca, a deep neural network built by DeepMind with the Universities of Venice Ca' Foscari, Oxford and Athens. Authors include Assael, Sommerschield, Prag and de Freitas. It restores missing text in damaged ancient Greek inscriptions and attributes them to place and date. Ithaca restored text with 62 percent accuracy alone and raised historians' accuracy from 25 to 72 percent when they worked with it. It attributed inscriptions geographically with 71 percent accuracy and dated them to within about 30 years. Its average date of 421 BC for a group of disputed Athenian decrees agreed with recent independent re-dating. The cited sources report accuracy studies and a public interface, not a specific inscription whose restoration epigraphers have since accepted.",
            "benefit_type": "Improved Task Accuracy"
          }
        },
        "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": "societal",
                "reason": "The sources give accuracy measurements and a prediction that agrees with independent re-dating of Athenian decrees. No discrete, verified downstream result is attributable to Ithaca."
              }
            ]
          }
        },
        "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": "Built to restore, geographically attribute and date ancient Greek inscriptions; the reported capability was that purpose."
          }
        },
        "provenance": {
          "kind": "legacy_record",
          "basis": "Preserved from the existing AIBD record; external sources were not rechecked.",
          "record_fields": [
            "unexpected",
            "purpose"
          ],
          "source_refs": []
        },
        "review_state": "record_mapped",
        "coverage": "partial"
      },
      "places[]": {
        "state": "value_present",
        "value": {
          "kind": "record_fields",
          "fields": {
            "developer_country": "United Kingdom"
          }
        },
        "provenance": {
          "kind": "legacy_record",
          "basis": "Preserved from the existing AIBD record; external sources were not rechecked.",
          "record_fields": [
            "developer_country"
          ],
          "source_refs": []
        },
        "review_state": "record_mapped",
        "coverage": "partial"
      },
      "purpose": {
        "state": "value_present",
        "value": {
          "kind": "record_fields",
          "fields": {
            "purpose": "Built to restore, geographically attribute and date ancient Greek inscriptions; the reported capability was that purpose."
          }
        },
        "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": "2022-03-09",
            "revisions": [
              {
                "date": "2026-10-20",
                "change": "Record drafted from the cited sources.",
                "editor": "AIBD"
              },
              {
                "date": "2026-10-20",
                "change": "Verification sweep: both sources were written by Ithaca's developers (DeepMind and Ca' Foscari), so the Ca' Foscari page is now marked vendor-authored and a Nature News & Views by Charlotte Roueché, who is not an author of the paper, is added as the independent source.",
                "source": "https://www.nature.com/articles/d41586-022-00641-2",
                "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 Knowledge Discovery 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://europepmc.org/article/PMC/PMC8907065; https://www.technologyreview.com/2025/07/23/1120574/deepmind-ai-aeneas-helps-historians-interpret-latin-inscriptions/",
                "editor": "AIBD"
              },
              {
                "date": "2026-10-21",
                "change": "Country of the developer added from the organization's own site or a registry.",
                "source": "https://en.wikipedia.org/wiki/Google_DeepMind",
                "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",
          "basis": "Preserved from the existing AIBD record; external sources were not rechecked.",
          "record_fields": [
            "date_observed",
            "revisions"
          ],
          "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": [
                "A large digitized corpus of inscriptions with known provenance and date",
                "A neural network trained on artificially damaged text",
                "Evaluation against held-out inscriptions and with historians in the loop"
              ],
              "must_hold": [
                "A verified event of a benefit created or restored would require an epigrapher-accepted restoration or re-dating attributable to the model and documented in a non-vendor source"
              ],
              "echoes": []
            }
          }
        },
        "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": "2026-10-20",
                "change": "Verification sweep: both sources were written by Ithaca's developers (DeepMind and Ca' Foscari), so the Ca' Foscari page is now marked vendor-authored and a Nature News & Views by Charlotte Roueché, who is not an author of the paper, is added as the independent source.",
                "source": "https://www.nature.com/articles/d41586-022-00641-2",
                "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 Knowledge Discovery 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://europepmc.org/article/PMC/PMC8907065; https://www.technologyreview.com/2025/07/23/1120574/deepmind-ai-aeneas-helps-historians-interpret-latin-inscriptions/",
                "editor": "AIBD"
              },
              {
                "date": "2026-10-21",
                "change": "Country of the developer added from the organization's own site or a registry.",
                "source": "https://en.wikipedia.org/wiki/Google_DeepMind",
                "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",
          "basis": "Preserved from the existing AIBD record; external sources were not rechecked.",
          "record_fields": [
            "revisions"
          ],
          "source_refs": []
        },
        "review_state": "record_mapped",
        "coverage": "partial"
      },
      "sector": {
        "state": "value_present",
        "value": {
          "kind": "record_fields",
          "fields": {
            "domain": "Humanities / epigraphy",
            "segment": "Academic research"
          }
        },
        "provenance": {
          "kind": "legacy_record",
          "basis": "Preserved from the existing AIBD record; external sources were not rechecked.",
          "record_fields": [
            "domain",
            "segment"
          ],
          "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": "peer-reviewed study",
                "citation": "Assael, Y., Sommerschield, T. et al. (2022). Restoring and attributing ancient texts using deep neural networks. Nature 603, 280–283.",
                "title": "Restoring and attributing ancient texts using deep neural networks",
                "url": "https://www.nature.com/articles/s41586-022-04448-z",
                "date": "2022-03-09",
                "written_by": "makers",
                "quote": "Ithaca's average predicted date for all of these decrees is 421 bc"
              },
              {
                "kind": "press release",
                "citation": "Ca' Foscari University of Venice (2022). AI to aid historians in restoring, placing and dating ancient texts. University news.",
                "title": "AI to aid historians in restoring, placing and dating ancient texts",
                "url": "https://www.unive.it/pag/16584/?tx_news_pi1%5Bnews%5D=12037",
                "date": "2022-03-09",
                "written_by": "makers",
                "quote": "have already used the model to shed light on current disputes in Greek history"
              },
              {
                "kind": "report",
                "citation": "Roueché, C. (2022). AI minds the gap and fills in missing Greek inscriptions. Nature, News & Views, 9 March 2022. doi:10.1038/d41586-022-00641-2",
                "title": "AI minds the gap and fills in missing Greek inscriptions",
                "url": "https://www.nature.com/articles/d41586-022-00641-2",
                "date": "2022-03-09",
                "written_by": "independent",
                "quote": "A new AI tool helps to fill in missing text and estimate the timeframe and geographical origin of ancient inscriptions."
              },
              {
                "kind": "news report",
                "citation": "Google DeepMind's new AI can help historians understand ancient Latin inscriptions. MIT Technology Review, 23 July 2025.",
                "title": "Google DeepMind's new AI can help historians understand ancient Latin inscriptions",
                "url": "https://www.technologyreview.com/2025/07/23/1120574/deepmind-ai-aeneas-helps-historians-interpret-latin-inscriptions/",
                "date": "2025-07-23",
                "written_by": "independent"
              }
            ]
          }
        },
        "provenance": {
          "kind": "legacy_record",
          "basis": "Preserved from the existing AIBD record; external sources were not rechecked.",
          "record_fields": [
            "evidence"
          ],
          "source_refs": []
        },
        "review_state": "record_mapped",
        "coverage": "partial"
      },
      "sources[].id": {
        "state": "value_present",
        "value": {
          "kind": "identifiers",
          "identifiers": [
            "AIBD-0017-source-0001",
            "AIBD-0017-source-0002",
            "AIBD-0017-source-0003",
            "AIBD-0017-source-0004"
          ],
          "role": "Existing evidence items, in their original order"
        },
        "provenance": {
          "kind": "curatorial",
          "basis": "Stable within the fingerprinted record version; no source content or source review has been added.",
          "record_fields": [],
          "source_refs": []
        },
        "review_state": "record_mapped",
        "coverage": "complete"
      },
      "sources[].language": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial"
      },
      "sources[].relationship": {
        "state": "value_present",
        "value": {
          "kind": "record_fields",
          "fields": {
            "evidence": [
              {
                "kind": "peer-reviewed study",
                "citation": "Assael, Y., Sommerschield, T. et al. (2022). Restoring and attributing ancient texts using deep neural networks. Nature 603, 280–283.",
                "title": "Restoring and attributing ancient texts using deep neural networks",
                "url": "https://www.nature.com/articles/s41586-022-04448-z",
                "date": "2022-03-09",
                "written_by": "makers",
                "quote": "Ithaca's average predicted date for all of these decrees is 421 bc"
              },
              {
                "kind": "press release",
                "citation": "Ca' Foscari University of Venice (2022). AI to aid historians in restoring, placing and dating ancient texts. University news.",
                "title": "AI to aid historians in restoring, placing and dating ancient texts",
                "url": "https://www.unive.it/pag/16584/?tx_news_pi1%5Bnews%5D=12037",
                "date": "2022-03-09",
                "written_by": "makers",
                "quote": "have already used the model to shed light on current disputes in Greek history"
              },
              {
                "kind": "report",
                "citation": "Roueché, C. (2022). AI minds the gap and fills in missing Greek inscriptions. Nature, News & Views, 9 March 2022. doi:10.1038/d41586-022-00641-2",
                "title": "AI minds the gap and fills in missing Greek inscriptions",
                "url": "https://www.nature.com/articles/d41586-022-00641-2",
                "date": "2022-03-09",
                "written_by": "independent",
                "quote": "A new AI tool helps to fill in missing text and estimate the timeframe and geographical origin of ancient inscriptions."
              },
              {
                "kind": "news report",
                "citation": "Google DeepMind's new AI can help historians understand ancient Latin inscriptions. MIT Technology Review, 23 July 2025.",
                "title": "Google DeepMind's new AI can help historians understand ancient Latin inscriptions",
                "url": "https://www.technologyreview.com/2025/07/23/1120574/deepmind-ai-aeneas-helps-historians-interpret-latin-inscriptions/",
                "date": "2025-07-23",
                "written_by": "independent"
              }
            ]
          }
        },
        "provenance": {
          "kind": "legacy_record",
          "basis": "Preserved from the existing AIBD record; external sources were not rechecked.",
          "record_fields": [
            "evidence"
          ],
          "source_refs": []
        },
        "review_state": "record_mapped",
        "coverage": "partial"
      },
      "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": {
        "state": "value_present",
        "value": {
          "kind": "record_fields",
          "fields": {
            "summary": "Ithaca restores and dates Greek inscriptions with measured accuracy gains; no specific accepted restoration verified.",
            "account": "On 9 March 2022 Nature published Ithaca, a deep neural network built by DeepMind with the Universities of Venice Ca' Foscari, Oxford and Athens. Authors include Assael, Sommerschield, Prag and de Freitas. It restores missing text in damaged ancient Greek inscriptions and attributes them to place and date. Ithaca restored text with 62 percent accuracy alone and raised historians' accuracy from 25 to 72 percent when they worked with it. It attributed inscriptions geographically with 71 percent accuracy and dated them to within about 30 years. Its average date of 421 BC for a group of disputed Athenian decrees agreed with recent independent re-dating. The cited sources report accuracy studies and a public interface, not a specific inscription whose restoration epigraphers have since accepted."
          }
        },
        "provenance": {
          "kind": "legacy_record",
          "basis": "Preserved from the existing AIBD record; external sources were not rechecked.",
          "record_fields": [
            "summary",
            "account"
          ],
          "source_refs": []
        },
        "review_state": "record_mapped",
        "coverage": "partial"
      },
      "systems[].autonomy_permissions": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial"
      },
      "systems[].component_role": {
        "state": "value_present",
        "value": {
          "kind": "text",
          "text": "Ithaca proposed missing text, dates, and places of origin for ancient inscriptions."
        },
        "provenance": {
          "kind": "legacy_record",
          "basis": "Restated the mechanical task already described in the existing system and AI-role fields. No new source review is claimed.",
          "record_fields": [
            "system",
            "ai_role"
          ],
          "source_refs": []
        },
        "review_state": "record_mapped",
        "coverage": "partial"
      },
      "systems[].deployment_configuration": {
        "state": "value_present",
        "value": {
          "kind": "text",
          "text": "Researchers used the 2022 Ithaca system."
        },
        "provenance": {
          "kind": "legacy_record",
          "basis": "Separated the model, software, hardware, or operation already described in the existing record. No new source review is claimed.",
          "record_fields": [
            "model_version",
            "system",
            "ai_role"
          ],
          "source_refs": []
        },
        "review_state": "record_mapped",
        "coverage": "partial"
      },
      "systems[].inputs_outputs": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial"
      },
      "systems[].model_name": {
        "state": "value_present",
        "value": {
          "kind": "text",
          "text": "Ithaca, a transformer-based neural network."
        },
        "provenance": {
          "kind": "legacy_record",
          "basis": "Separated the model, software, hardware, or operation already described in the existing record. No new source review is claimed.",
          "record_fields": [
            "model_version",
            "system",
            "ai_role"
          ],
          "source_refs": []
        },
        "review_state": "record_mapped",
        "coverage": "partial"
      },
      "systems[].model_version": {
        "state": "value_present",
        "value": {
          "kind": "record_fields",
          "fields": {
            "model_version": "Ithaca (2022)"
          }
        },
        "provenance": {
          "kind": "legacy_record",
          "basis": "Preserved from the existing AIBD record; external sources were not rechecked.",
          "record_fields": [
            "model_version"
          ],
          "source_refs": []
        },
        "review_state": "record_mapped",
        "coverage": "partial",
        "display": {
          "answer": "Exact release not established.",
          "editor_note": ""
        }
      },
      "systems[].product_name": {
        "state": "value_present",
        "value": {
          "kind": "record_fields",
          "fields": {
            "system": "Ithaca (transformer-based deep neural network for epigraphic restoration and attribution)"
          }
        },
        "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": "DeepMind with Ca' Foscari University of Venice, University of Oxford and Athens University of Economics and Business"
          }
        },
        "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": "Ithaca: deep neural network for restoring, placing and dating ancient Greek inscriptions (capability study)",
            "headline": "Ithaca raises historians' accuracy from 25 to 72 percent"
          }
        },
        "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": [
            "302890ae1f7d56f3ea61c181dd07fed38ed62453610ee0492cdfece78ec510bf"
          ],
          "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": "On 9 March 2022 Nature published Ithaca, a deep neural network built by DeepMind with the Universities of Venice Ca' Foscari, Oxford and Athens. Authors include Assael, Sommerschield, Prag and de Freitas. It restores missing text in damaged ancient Greek inscriptions and attributes them to place and date. Ithaca restored text with 62 percent accuracy alone and raised historians' accuracy from 25 to 72 percent when they worked with it. It attributed inscriptions geographically with 71 percent accuracy and dated them to within about 30 years. Its average date of 421 BC for a group of disputed Athenian decrees agreed with recent independent re-dating. The cited sources report accuracy studies and a public interface, not a specific inscription whose restoration epigraphers have since accepted.",
            "replication": {
              "produced_by": [
                "A large digitized corpus of inscriptions with known provenance and date",
                "A neural network trained on artificially damaged text",
                "Evaluation against held-out inscriptions and with historians in the loop"
              ],
              "must_hold": [
                "A verified event of a benefit created or restored would require an epigrapher-accepted restoration or re-dating attributable to the model and documented in a non-vendor source"
              ],
              "echoes": []
            }
          }
        },
        "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": 31,
      "reviewed": 0,
      "record_mapped": 31,
      "not_assessed": 60,
      "complete": false,
      "by_state": {
        "value_present": 31,
        "not_reported": 0,
        "unresolved": 0,
        "not_assessed": 60,
        "not_applicable": 0,
        "withheld": 0,
        "source_unavailable": 0
      }
    }
  }
}
