{
  "id": "AIBD-0034",
  "title": "Blind and low-vision participants complete everyday tasks with Be My AI in peer-reviewed interview studies",
  "summary": "Peer-reviewed interview studies record blind users completing everyday tasks with Be My AI, e.g., airport gate numbers, a handwritten check.",
  "date_observed": "2023-09-25",
  "date_basis": "unassessed",
  "date_precision": "unassessed",
  "benefit_status": null,
  "source_collection": null,
  "source_entry": null,
  "account": "Xie and colleagues at Pennsylvania State University interviewed 14 visually impaired users of Be My AI. That is the large-multimodal-model image-description feature of the Be My Eyes app. They analyzed image descriptions gathered from 25 September 2023 to 31 March 2024. The paper appeared at CHI 2025. Individual uses are recorded. “P2 utilized it to identify gate numbers at the airport”. “P10 used it to recognize her surroundings when disoriented in her neighborhood”. P13 received a description locating “a hair tie in the upper right hand corner” of a carpet. A separate ASSETS 2025 interview study of 21 participants (November 2024 to January 2025) records P9 using Be My AI to read a handwritten check. Be My AI was publicly released on 25 September 2023. That starts the first paper's social-media collection window. The interview participants' uses are undated and outcomes self-reported.",
  "category": "benefit created",
  "unexpected": false,
  "purpose": "Be My AI was built to describe photographed scenes and objects to blind and low-vision users. These task completions were that purpose. Xie et al. state that Be My AI is built on OpenAI's GPT-4 models.",
  "system": "Be My AI (large-multimodal-model image description in the Be My Eyes app)",
  "model_version": "GPT-4 (per Xie et al. 2025)",
  "developer": "Be My Eyes (feature built on OpenAI's GPT-4)",
  "deployer": "Be My Eyes (consumer app used by study participants)",
  "location": null,
  "domain": "Accessibility / visual assistance",
  "segment": "Blind and low-vision users",
  "benefit_type": "Improved Accessibility",
  "taxonomy_note": null,
  "registry_ids": {},
  "ratings": [
    {
      "group": "personal",
      "depth": 1,
      "reason": "Everyday tasks (gate numbers, orientation, locating a dropped item, reading a check) completed independently by identifiable participants. Small, self-reported, and the same papers document frequent errors."
    }
  ],
  "ai_role": {
    "status": "assessed",
    "statement": "The multimodal model generated the descriptions participants acted on. No independent per-task accuracy check was made. Participants themselves reported risk (P9: “I knew there was a risk”)."
  },
  "evidence": [
    {
      "kind": "peer-reviewed study",
      "citation": "Xie J, Yu R, Zhang H, Billah SM, Lee S, Carroll JM (2025). Beyond Visual Perception: Insights from Smartphone Interaction of Visually Impaired Users with Large Multimodal Models. Proc. CHI 2025 (PMC12338113).",
      "title": "Beyond Visual Perception: Insights from Smartphone Interaction of Visually Impaired Users with Large Multimodal Models",
      "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC12338113/",
      "date": "2025-04-25",
      "written_by": "independent",
      "quote": "P10 used it to recognize her surroundings when disoriented in her neighborhood"
    },
    {
      "kind": "preprint",
      "citation": "Sharma T, Tseng Y-Y, Zhang L, Ide A, Mack KA, Findlater L, Gurari D, Wang Y (2025). 'Before, I Asked My Mom, Now I Ask ChatGPT': Visual Privacy Management with Generative AI for Blind and Low-Vision People. arXiv:2507.00286 (published ASSETS 2025).",
      "title": "Visual Privacy Management with Generative AI for Blind and Low Vision People",
      "url": "https://arxiv.org/html/2507.00286v1",
      "date": "2025-06-30",
      "written_by": "independent",
      "quote": "I have used Be My Eyes AI to read a handwritten check in a pinch, but I knew there was a risk."
    },
    {
      "kind": "other",
      "citation": "Penn State Pure research record (2025). Publication venue entry for Sharma et al.",
      "title": "Penn State Pure: 'Before, I Asked My Mom, Now I Ask ChatGPT'",
      "url": "https://pure.psu.edu/en/publications/before-i-asked-my-mom-now-i-ask-chatgpt-visual-privacy-management/",
      "date": "2025-10-22",
      "written_by": "independent",
      "quote": "ASSETS 2025 - Proceedings of the 27th International ACM SIGACCESS Conference on Computers and Accessibility"
    }
  ],
  "replication": {
    "produced_by": [
      "Smartphone photograph submitted to Be My AI by a blind or low-vision user",
      "Task in a familiar or low-stakes setting",
      "User interprets a text description and acts on it"
    ],
    "must_hold": [
      "The model does not hallucinate in the specific scene (both studies document hallucinations)",
      "User cross-checks high-stakes outputs such as medication or finances"
    ],
    "echoes": [
      {
        "date": "2024-11-01",
        "setting": "ASSETS 2025 interview study, 21 US participants, interviews November 2024 to January 2025 (first day of month used); P9 read a handwritten check",
        "model_version": null,
        "outcome": "recurred",
        "source": "https://arxiv.org/html/2507.00286v1"
      }
    ]
  },
  "paired_incident": {
    "database": "AI Incident Database",
    "identifier": "830",
    "title": "Error-Prone AI Accessibility Tools Reportedly Lead to Navigation Issues for Blind Internet Users",
    "url": "https://incidentdatabase.ai/cite/830",
    "severity": null,
    "group": "personal",
    "basis": "Same stakeholders and task: AI describing visual content for blind users; Be My AI read gate numbers and a check, while AI accessibility overlays mislabelled web content, misleading blind and visually impaired people such as Jakob Rosin."
  },
  "headline": "Be My AI reads airport gate numbers and a check for blind users",
  "metric": null,
  "comparator": {
    "against": "asking a sighted family member or friend to check a photo",
    "before": "Before GenAI, I would often send it to my mom or friend to check for me.",
    "after": "Now I Ask ChatGPT",
    "basis": "assumed",
    "quote": "I don't want to give a weird impression when posting my picture. Before GenAI, I would often send it to my mom or friend to check for me.",
    "url": "https://arxiv.org/html/2507.00286v1"
  },
  "later": {
    "status": "persisted",
    "date": "2025-06-30",
    "note": "An independent interview study run from November 2024 to January 2025 recorded blind participants still using Be My AI for everyday reading tasks such as a handwritten check.",
    "quote": "I've used Be My Eyes AI to read a handwritten check in a pinch, but I knew there was a risk—it could've exposed bank info",
    "url": "https://arxiv.org/html/2507.00286v1"
  },
  "value": null,
  "developer_country": "United States",
  "deployer_country": "United States",
  "unrated_because": null,
  "brief_notes": null,
  "brief": null,
  "revisions": [
    {
      "date": "2026-10-20",
      "change": "Record drafted from the cited sources.",
      "source": null,
      "editor": "AIBD"
    },
    {
      "date": "2026-10-20",
      "change": "Paired with AI Incident Database incident 830 (medium confidence); severity not given by the source.",
      "source": "https://incidentdatabase.ai/cite/830",
      "editor": "AIBD"
    },
    {
      "date": "2026-10-20",
      "change": "Added the GPT-4 basis that Xie et al. state (the record had said no source names the model vendor), removed P1's pregnancy-test reading from the echo because the source reports inconsistent results rather than a completed task, explained that the observation date is Be My AI's release date and that the interview uses are undated, and matched the arXiv v1 title's unhyphenated “Low Vision”.",
      "source": "https://pmc.ncbi.nlm.nih.gov/articles/PMC12338113/",
      "editor": "AIBD"
    },
    {
      "date": "2026-10-21",
      "change": "Added what the benefit is measured against, what happened since from the cited sources.",
      "source": "https://arxiv.org/html/2507.00286v1; https://arxiv.org/html/2507.00286v1",
      "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://www.bemyeyes.com/terms; https://www.bemyeyes.com/terms",
      "editor": "AIBD"
    },
    {
      "date": "2026-10-21",
      "change": "Prose pass: a rating reason, account, purpose, 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 peer-reviewed study, one independent preprint and one other independent source. The cited evidence includes no finding by a regulator or court.",
  "profile": {
    "case_id": "AIBD-0034",
    "catalog_version": "1.0.0",
    "source_fingerprint": "6ddf264b722b43d9ee85c0372ded73b64a0a3670d062ea0f2fd3fdda94996f59",
    "collection_refs": [
      {
        "collection_id": "aibd",
        "version": "snapshot-8bf7cfec9cb185aa"
      },
      {
        "collection_id": "aibd",
        "version": "2026-10-25"
      }
    ],
    "source_ids": [
      "AIBD-0034-source-0001",
      "AIBD-0034-source-0002",
      "AIBD-0034-source-0003"
    ],
    "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": "Be My Eyes (feature built on OpenAI's GPT-4)",
            "deployer": "Be My Eyes (consumer app used by study participants)"
          }
        },
        "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": "The multimodal model generated the descriptions participants acted on. No independent per-task accuracy check was made. Participants themselves reported risk (P9: “I knew there was a risk”)."
            }
          }
        },
        "provenance": {
          "kind": "legacy_record",
          "basis": "Preserved from the existing AIBD record; external sources were not rechecked.",
          "record_fields": [
            "ai_role"
          ],
          "source_refs": []
        },
        "review_state": "record_mapped",
        "coverage": "partial"
      },
      "ai_relationship": {
        "state": "value_present",
        "value": {
          "kind": "record_fields",
          "fields": {
            "category": "benefit created",
            "ai_role": {
              "status": "assessed",
              "statement": "The multimodal model generated the descriptions participants acted on. No independent per-task accuracy check was made. Participants themselves reported risk (P9: “I knew there was a risk”)."
            }
          }
        },
        "provenance": {
          "kind": "legacy_record",
          "basis": "Preserved from the existing AIBD record; external sources were not rechecked.",
          "record_fields": [
            "ai_role",
            "category"
          ],
          "source_refs": []
        },
        "review_state": "record_mapped",
        "coverage": "partial"
      },
      "assessments[].alternatives": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial"
      },
      "assessments[].assessor_date": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial"
      },
      "assessments[].depth": {
        "state": "value_present",
        "value": {
          "kind": "record_fields",
          "fields": {
            "ratings": [
              {
                "group": "personal",
                "depth": 1,
                "reason": "Everyday tasks (gate numbers, orientation, locating a dropped item, reading a check) completed independently by identifiable participants. Small, self-reported, and the same papers document frequent errors."
              }
            ]
          }
        },
        "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[].display_confidence": {
        "state": "value_present",
        "value": {
          "kind": "structured",
          "data": {
            "confidence": "high",
            "evidence_word": "studied",
            "claim_status": "Independently studied",
            "basis": "Sources: one independent peer-reviewed study, one independent preprint and one other independent source. 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": "personal",
                "depth": 1,
                "reason": "Everyday tasks (gate numbers, orientation, locating a dropped item, reading a check) completed independently by identifiable participants. Small, self-reported, and the same papers document frequent errors."
              }
            ]
          }
        },
        "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": "personal",
                "depth": 1,
                "reason": "Everyday tasks (gate numbers, orientation, locating a dropped item, reading a check) completed independently by identifiable participants. Small, self-reported, and the same papers document frequent errors."
              }
            ]
          }
        },
        "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": "value_present",
        "value": {
          "kind": "structured",
          "data": {
            "answer": "Same stakeholders and task: AI describing visual content for blind users; Be My AI read gate numbers and a check, while AI accessibility overlays mislabelled web content, misleading blind and visually impaired people such as Jakob Rosin.",
            "links": [
              {
                "database": "AI Incident Database",
                "identifier": "830",
                "title": "Error-Prone AI Accessibility Tools Reportedly Lead to Navigation Issues for Blind Internet Users",
                "url": "https://incidentdatabase.ai/cite/830",
                "group": "personal",
                "basis": "Same stakeholders and task: AI describing visual content for blind users; Be My AI read gate numbers and a check, while AI accessibility overlays mislabelled web content, misleading blind and visually impaired people such as Jakob Rosin.",
                "relation": "comparable_harm"
              }
            ]
          }
        },
        "provenance": {
          "kind": "legacy_record",
          "basis": "Existing paired_incident classification; no new source review.",
          "record_fields": [
            "paired_incident"
          ],
          "source_refs": []
        },
        "review_state": "record_mapped",
        "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",
        "review_state": "not_assessed",
        "coverage": "partial",
        "family": "evaluations"
      },
      "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": "value_present",
        "value": {
          "kind": "record_fields",
          "fields": {
            "paired_incident": {
              "database": "AI Incident Database",
              "identifier": "830",
              "title": "Error-Prone AI Accessibility Tools Reportedly Lead to Navigation Issues for Blind Internet Users",
              "url": "https://incidentdatabase.ai/cite/830",
              "group": "personal",
              "basis": "Same stakeholders and task: AI describing visual content for blind users; Be My AI read gate numbers and a check, while AI accessibility overlays mislabelled web content, misleading blind and visually impaired people such as Jakob Rosin."
            }
          }
        },
        "provenance": {
          "kind": "legacy_record",
          "basis": "Preserved from the existing AIBD record; external sources were not rechecked.",
          "record_fields": [
            "paired_incident"
          ],
          "source_refs": []
        },
        "review_state": "record_mapped",
        "coverage": "partial"
      },
      "follow_up[]": {
        "state": "value_present",
        "value": {
          "kind": "record_fields",
          "fields": {
            "later": {
              "status": "persisted",
              "date": "2025-06-30",
              "note": "An independent interview study run from November 2024 to January 2025 recorded blind participants still using Be My AI for everyday reading tasks such as a handwritten check.",
              "quote": "I've used Be My Eyes AI to read a handwritten check in a pinch, but I knew there was a risk—it could've exposed bank info",
              "url": "https://arxiv.org/html/2507.00286v1"
            }
          }
        },
        "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-0034"
          }
        },
        "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 Accessibility"
          }
        },
        "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": "Xie and colleagues at Pennsylvania State University interviewed 14 visually impaired users of Be My AI. That is the large-multimodal-model image-description feature of the Be My Eyes app. They analyzed image descriptions gathered from 25 September 2023 to 31 March 2024. The paper appeared at CHI 2025. Individual uses are recorded. “P2 utilized it to identify gate numbers at the airport”. “P10 used it to recognize her surroundings when disoriented in her neighborhood”. P13 received a description locating “a hair tie in the upper right hand corner” of a carpet. A separate ASSETS 2025 interview study of 21 participants (November 2024 to January 2025) records P9 using Be My AI to read a handwritten check. Be My AI was publicly released on 25 September 2023. That starts the first paper's social-media collection window. The interview participants' uses are undated and outcomes self-reported.",
            "benefit_type": "Improved Accessibility"
          }
        },
        "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",
                "depth": 1,
                "reason": "Everyday tasks (gate numbers, orientation, locating a dropped item, reading a check) completed independently by identifiable participants. Small, self-reported, and the same papers document frequent errors."
              }
            ]
          }
        },
        "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": "Be My AI was built to describe photographed scenes and objects to blind and low-vision users. These task completions were that purpose. Xie et al. state that Be My AI is built on OpenAI's GPT-4 models."
          }
        },
        "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 States",
            "deployer_country": "United States"
          }
        },
        "provenance": {
          "kind": "legacy_record",
          "basis": "Preserved from the existing AIBD record; external sources were not rechecked.",
          "record_fields": [
            "developer_country",
            "deployer_country"
          ],
          "source_refs": []
        },
        "review_state": "record_mapped",
        "coverage": "partial"
      },
      "purpose": {
        "state": "value_present",
        "value": {
          "kind": "record_fields",
          "fields": {
            "purpose": "Be My AI was built to describe photographed scenes and objects to blind and low-vision users. These task completions were that purpose. Xie et al. state that Be My AI is built on OpenAI's GPT-4 models."
          }
        },
        "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": "2023-09-25",
            "revisions": [
              {
                "date": "2026-10-20",
                "change": "Record drafted from the cited sources.",
                "editor": "AIBD"
              },
              {
                "date": "2026-10-20",
                "change": "Paired with AI Incident Database incident 830 (medium confidence); severity not given by the source.",
                "source": "https://incidentdatabase.ai/cite/830",
                "editor": "AIBD"
              },
              {
                "date": "2026-10-20",
                "change": "Added the GPT-4 basis that Xie et al. state (the record had said no source names the model vendor), removed P1's pregnancy-test reading from the echo because the source reports inconsistent results rather than a completed task, explained that the observation date is Be My AI's release date and that the interview uses are undated, and matched the arXiv v1 title's unhyphenated “Low Vision”.",
                "source": "https://pmc.ncbi.nlm.nih.gov/articles/PMC12338113/",
                "editor": "AIBD"
              },
              {
                "date": "2026-10-21",
                "change": "Added what the benefit is measured against, what happened since from the cited sources.",
                "source": "https://arxiv.org/html/2507.00286v1; https://arxiv.org/html/2507.00286v1",
                "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://www.bemyeyes.com/terms; https://www.bemyeyes.com/terms",
                "editor": "AIBD"
              },
              {
                "date": "2026-10-21",
                "change": "Prose pass: a rating reason, account, purpose, 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": "value_present",
        "value": {
          "kind": "record_fields",
          "fields": {
            "replication": {
              "produced_by": [
                "Smartphone photograph submitted to Be My AI by a blind or low-vision user",
                "Task in a familiar or low-stakes setting",
                "User interprets a text description and acts on it"
              ],
              "must_hold": [
                "The model does not hallucinate in the specific scene (both studies document hallucinations)",
                "User cross-checks high-stakes outputs such as medication or finances"
              ],
              "echoes": [
                {
                  "date": "2024-11-01",
                  "setting": "ASSETS 2025 interview study, 21 US participants, interviews November 2024 to January 2025 (first day of month used); P9 read a handwritten check",
                  "outcome": "recurred",
                  "source": "https://arxiv.org/html/2507.00286v1"
                }
              ]
            }
          }
        },
        "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"
      },
      "replication.conditions": {
        "state": "value_present",
        "value": {
          "kind": "record_fields",
          "fields": {
            "replication": {
              "produced_by": [
                "Smartphone photograph submitted to Be My AI by a blind or low-vision user",
                "Task in a familiar or low-stakes setting",
                "User interprets a text description and acts on it"
              ],
              "must_hold": [
                "The model does not hallucinate in the specific scene (both studies document hallucinations)",
                "User cross-checks high-stakes outputs such as medication or finances"
              ],
              "echoes": [
                {
                  "date": "2024-11-01",
                  "setting": "ASSETS 2025 interview study, 21 US participants, interviews November 2024 to January 2025 (first day of month used); P9 read a handwritten check",
                  "outcome": "recurred",
                  "source": "https://arxiv.org/html/2507.00286v1"
                }
              ]
            }
          }
        },
        "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": "Paired with AI Incident Database incident 830 (medium confidence); severity not given by the source.",
                "source": "https://incidentdatabase.ai/cite/830",
                "editor": "AIBD"
              },
              {
                "date": "2026-10-20",
                "change": "Added the GPT-4 basis that Xie et al. state (the record had said no source names the model vendor), removed P1's pregnancy-test reading from the echo because the source reports inconsistent results rather than a completed task, explained that the observation date is Be My AI's release date and that the interview uses are undated, and matched the arXiv v1 title's unhyphenated “Low Vision”.",
                "source": "https://pmc.ncbi.nlm.nih.gov/articles/PMC12338113/",
                "editor": "AIBD"
              },
              {
                "date": "2026-10-21",
                "change": "Added what the benefit is measured against, what happened since from the cited sources.",
                "source": "https://arxiv.org/html/2507.00286v1; https://arxiv.org/html/2507.00286v1",
                "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://www.bemyeyes.com/terms; https://www.bemyeyes.com/terms",
                "editor": "AIBD"
              },
              {
                "date": "2026-10-21",
                "change": "Prose pass: a rating reason, account, purpose, 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": "Accessibility / visual assistance",
            "segment": "Blind and low-vision users"
          }
        },
        "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": "Xie J, Yu R, Zhang H, Billah SM, Lee S, Carroll JM (2025). Beyond Visual Perception: Insights from Smartphone Interaction of Visually Impaired Users with Large Multimodal Models. Proc. CHI 2025 (PMC12338113).",
                "title": "Beyond Visual Perception: Insights from Smartphone Interaction of Visually Impaired Users with Large Multimodal Models",
                "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC12338113/",
                "date": "2025-04-25",
                "written_by": "independent",
                "quote": "P10 used it to recognize her surroundings when disoriented in her neighborhood"
              },
              {
                "kind": "preprint",
                "citation": "Sharma T, Tseng Y-Y, Zhang L, Ide A, Mack KA, Findlater L, Gurari D, Wang Y (2025). 'Before, I Asked My Mom, Now I Ask ChatGPT': Visual Privacy Management with Generative AI for Blind and Low-Vision People. arXiv:2507.00286 (published ASSETS 2025).",
                "title": "Visual Privacy Management with Generative AI for Blind and Low Vision People",
                "url": "https://arxiv.org/html/2507.00286v1",
                "date": "2025-06-30",
                "written_by": "independent",
                "quote": "I have used Be My Eyes AI to read a handwritten check in a pinch, but I knew there was a risk."
              },
              {
                "kind": "other",
                "citation": "Penn State Pure research record (2025). Publication venue entry for Sharma et al.",
                "title": "Penn State Pure: 'Before, I Asked My Mom, Now I Ask ChatGPT'",
                "url": "https://pure.psu.edu/en/publications/before-i-asked-my-mom-now-i-ask-chatgpt-visual-privacy-management/",
                "date": "2025-10-22",
                "written_by": "independent",
                "quote": "ASSETS 2025 - Proceedings of the 27th International ACM SIGACCESS Conference on Computers and Accessibility"
              }
            ]
          }
        },
        "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-0034-source-0001",
            "AIBD-0034-source-0002",
            "AIBD-0034-source-0003"
          ],
          "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": "Xie J, Yu R, Zhang H, Billah SM, Lee S, Carroll JM (2025). Beyond Visual Perception: Insights from Smartphone Interaction of Visually Impaired Users with Large Multimodal Models. Proc. CHI 2025 (PMC12338113).",
                "title": "Beyond Visual Perception: Insights from Smartphone Interaction of Visually Impaired Users with Large Multimodal Models",
                "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC12338113/",
                "date": "2025-04-25",
                "written_by": "independent",
                "quote": "P10 used it to recognize her surroundings when disoriented in her neighborhood"
              },
              {
                "kind": "preprint",
                "citation": "Sharma T, Tseng Y-Y, Zhang L, Ide A, Mack KA, Findlater L, Gurari D, Wang Y (2025). 'Before, I Asked My Mom, Now I Ask ChatGPT': Visual Privacy Management with Generative AI for Blind and Low-Vision People. arXiv:2507.00286 (published ASSETS 2025).",
                "title": "Visual Privacy Management with Generative AI for Blind and Low Vision People",
                "url": "https://arxiv.org/html/2507.00286v1",
                "date": "2025-06-30",
                "written_by": "independent",
                "quote": "I have used Be My Eyes AI to read a handwritten check in a pinch, but I knew there was a risk."
              },
              {
                "kind": "other",
                "citation": "Penn State Pure research record (2025). Publication venue entry for Sharma et al.",
                "title": "Penn State Pure: 'Before, I Asked My Mom, Now I Ask ChatGPT'",
                "url": "https://pure.psu.edu/en/publications/before-i-asked-my-mom-now-i-ask-chatgpt-visual-privacy-management/",
                "date": "2025-10-22",
                "written_by": "independent",
                "quote": "ASSETS 2025 - Proceedings of the 27th International ACM SIGACCESS Conference on Computers and Accessibility"
              }
            ]
          }
        },
        "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": "Peer-reviewed interview studies record blind users completing everyday tasks with Be My AI, e.g., airport gate numbers, a handwritten check.",
            "account": "Xie and colleagues at Pennsylvania State University interviewed 14 visually impaired users of Be My AI. That is the large-multimodal-model image-description feature of the Be My Eyes app. They analyzed image descriptions gathered from 25 September 2023 to 31 March 2024. The paper appeared at CHI 2025. Individual uses are recorded. “P2 utilized it to identify gate numbers at the airport”. “P10 used it to recognize her surroundings when disoriented in her neighborhood”. P13 received a description locating “a hair tie in the upper right hand corner” of a carpet. A separate ASSETS 2025 interview study of 21 participants (November 2024 to January 2025) records P9 using Be My AI to read a handwritten check. Be My AI was publicly released on 25 September 2023. That starts the first paper's social-media collection window. The interview participants' uses are undated and outcomes self-reported."
          }
        },
        "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": "The model described images for people with impaired vision."
        },
        "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": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial"
      },
      "systems[].inputs_outputs": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial"
      },
      "systems[].model_name": {
        "state": "value_present",
        "value": {
          "kind": "text",
          "text": "GPT-4."
        },
        "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": "GPT-4 (per Xie et al. 2025)"
          }
        },
        "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": "Be My AI (large-multimodal-model image description in the Be My Eyes app)"
          }
        },
        "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": "Be My Eyes (feature built on OpenAI's GPT-4)"
          }
        },
        "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": "Blind and low-vision participants complete everyday tasks with Be My AI in peer-reviewed interview studies",
            "headline": "Be My AI reads airport gate numbers and a check for blind users"
          }
        },
        "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": [
            "6ddf264b722b43d9ee85c0372ded73b64a0a3670d062ea0f2fd3fdda94996f59"
          ],
          "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": "Xie and colleagues at Pennsylvania State University interviewed 14 visually impaired users of Be My AI. That is the large-multimodal-model image-description feature of the Be My Eyes app. They analyzed image descriptions gathered from 25 September 2023 to 31 March 2024. The paper appeared at CHI 2025. Individual uses are recorded. “P2 utilized it to identify gate numbers at the airport”. “P10 used it to recognize her surroundings when disoriented in her neighborhood”. P13 received a description locating “a hair tie in the upper right hand corner” of a carpet. A separate ASSETS 2025 interview study of 21 participants (November 2024 to January 2025) records P9 using Be My AI to read a handwritten check. Be My AI was publicly released on 25 September 2023. That starts the first paper's social-media collection window. The interview participants' uses are undated and outcomes self-reported.",
            "replication": {
              "produced_by": [
                "Smartphone photograph submitted to Be My AI by a blind or low-vision user",
                "Task in a familiar or low-stakes setting",
                "User interprets a text description and acts on it"
              ],
              "must_hold": [
                "The model does not hallucinate in the specific scene (both studies document hallucinations)",
                "User cross-checks high-stakes outputs such as medication or finances"
              ],
              "echoes": [
                {
                  "date": "2024-11-01",
                  "setting": "ASSETS 2025 interview study, 21 US participants, interviews November 2024 to January 2025 (first day of month used); P9 read a handwritten check",
                  "outcome": "recurred",
                  "source": "https://arxiv.org/html/2507.00286v1"
                }
              ]
            }
          }
        },
        "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": 34,
      "reviewed": 0,
      "record_mapped": 34,
      "not_assessed": 57,
      "complete": false,
      "by_state": {
        "value_present": 34,
        "not_reported": 0,
        "unresolved": 0,
        "not_assessed": 57,
        "not_applicable": 0,
        "withheld": 0,
        "source_unavailable": 0
      }
    }
  }
}
