{
  "id": "AIBD-0594",
  "title": "Open Data Labs reports fashion-preference prediction from consented data",
  "summary": "Open Data Labs says that an unnamed retailer improved fashion-preference prediction using consented data; the provider supplies no accuracy values or independent evaluation.",
  "date_observed": "2026-10-27",
  "date_basis": "source access",
  "date_precision": "day",
  "benefit_status": "reported",
  "source_collection": "AI Opportunity Inventory",
  "source_entry": "public-bank-row-11",
  "account": "Open Data Labs describes a collaboration with the research unit of an unnamed Fortune 500 fashion retailer. It says that datasets from tens of thousands of consenting contributors linked listening behavior with fashion preferences. According to the provider, the retailer built and internally validated a prediction model against demographic and behavioral baselines, enabling recommendations for customers without purchase history. The case study does not disclose the retailer, model, study date, sample sizes for testing, accuracy values, or independent review. This is a specific downstream application mentioned within the broader inventory submission, rather than a claim that the data infrastructure itself proves social benefit.",
  "category": "general benefit",
  "unexpected": false,
  "purpose": "The model is intended to personalize fashion recommendations for new customers who have no purchase history.",
  "system": "Undisclosed fashion-preference prediction model using Open Data Labs Primary Source Datasets",
  "model_version": null,
  "developer": "Unnamed retailer research unit; Open Data Labs supplied data",
  "deployer": "Unnamed Fortune 500 fashion retailer",
  "location": null,
  "domain": "Retail personalization and fashion recommendations",
  "segment": null,
  "benefit_type": "Product Personalization",
  "taxonomy_note": null,
  "registry_ids": {},
  "ratings": [
    {
      "group": "organizational",
      "depth": null,
      "reason": "The reviewed material does not establish the magnitude of a benefit for this group."
    },
    {
      "group": "personal",
      "depth": null,
      "reason": "The reviewed material does not establish the magnitude of a benefit for this group."
    }
  ],
  "ai_role": {
    "status": "assessed",
    "statement": "The retailer reportedly used a predictive model to infer preferences from behavior. Open Data Labs supplied training data. The source does not separate model effects from data effects or quantify downstream benefits."
  },
  "evidence": [
    {
      "kind": "report",
      "citation": "Fortune 500 fashion brand. Undated. https://www.opendatalabs.com/case-studies/fortune-500-fashion-brand. Accessed October 27, 2026.",
      "title": "Fortune 500 fashion brand",
      "url": "https://www.opendatalabs.com/case-studies/fortune-500-fashion-brand",
      "date": null,
      "written_by": "makers",
      "quote": null
    }
  ],
  "replication": {
    "produced_by": [
      "Open Data Labs supplied consented behavioral datasets, and the unnamed retailer developed and tested the model internally."
    ],
    "must_hold": [
      "The model, testing population, baseline methods, and accuracy results must be disclosed before the reported improvement can be independently checked."
    ],
    "echoes": []
  },
  "paired_incident": null,
  "headline": "A retailer predicts style preferences from consented data",
  "metric": null,
  "comparator": null,
  "later": {
    "status": "unknown",
    "date": null,
    "note": "Subsequent deployment and benefit outcomes have not been established by the reviewed sources.",
    "quote": null,
    "url": null
  },
  "value": null,
  "developer_country": null,
  "deployer_country": null,
  "unrated_because": "The reviewed sources do not support a numerical benefit-depth assessment.",
  "brief_notes": null,
  "brief": null,
  "revisions": [
    {
      "date": "2026-10-27",
      "change": "Added the case from reviewed public sources, with source lineage and limits on benefit claims.",
      "source": "https://www.opendatalabs.com/case-studies/fortune-500-fashion-brand",
      "editor": "AIBD"
    }
  ],
  "confidence": "claimed",
  "shade": "low",
  "claim_status": "The makers' claim",
  "claim_basis": "Sources: the makers' report. The cited evidence includes no independent source and no finding by a regulator or court.",
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    "case_id": "AIBD-0594",
    "catalog_version": "1.0.0",
    "source_fingerprint": "a3a6b227f522aa39b0263d2ea3899d5bca9c580c7bed74dc269d9f73acd3cbfc",
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      {
        "collection_id": "aibd",
        "version": "2026-10-25"
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              "No test results, quantified effect, date, or identified client supplied.",
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      "purpose": {
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          "kind": "text",
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      "replication.attempts[]": {
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        "review_state": "not_assessed",
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        "value": {
          "kind": "record_fields",
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              "produced_by": [
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                "The model, testing population, baseline methods, and accuracy results must be disclosed before the reported improvement can be independently checked."
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              "echoes": []
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        "provenance": {
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        "provenance": {
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      "sector": {
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          "kind": "record_fields",
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            "deployment_stage": "reported research application; deployment details incomplete",
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        "family": "strategic assessments"
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        "coverage": "complete"
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      "systems[].autonomy_permissions": {
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        "review_state": "not_assessed",
        "coverage": "partial"
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        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial"
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        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial"
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        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial"
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      "systems[].model_name": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial"
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      "systems[].model_version": {
        "state": "not_assessed",
        "review_state": "not_assessed",
        "coverage": "partial"
      },
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        "value": {
          "kind": "text",
          "text": "Undisclosed fashion-preference prediction model using Open Data Labs Primary Source Datasets"
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          "basis": "Reviewed the qualified case account against the retained original sources and their specific contributions.",
          "record_fields": [],
          "source_refs": [
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          "reviewed_on": "2026-10-27"
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        "review_state": "reviewed",
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      "systems[].provider": {
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        "coverage": "partial"
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      "taxonomy_crosswalks[]": {
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        "review_state": "not_assessed",
        "coverage": "partial"
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      "title": {
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        "value": {
          "kind": "text",
          "text": "Open Data Labs reports fashion-preference prediction from consented data"
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          "reviewed_on": "2026-10-27"
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        "review_state": "reviewed",
        "coverage": "complete"
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      "version": {
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        "provenance": {
          "kind": "derivation",
          "basis": "Computed the version from the normalized current core record.",
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          "source_refs": [],
          "reviewed_on": "2026-10-27"
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        "review_state": "reviewed",
        "coverage": "complete"
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      "workflow": {
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        "value": {
          "kind": "record_fields",
          "fields": {
            "account": "Open Data Labs describes a collaboration with the research unit of an unnamed Fortune 500 fashion retailer. It says that datasets from tens of thousands of consenting contributors linked listening behavior with fashion preferences. According to the provider, the retailer built and internally validated a prediction model against demographic and behavioral baselines, enabling recommendations for customers without purchase history. The case study does not disclose the retailer, model, study date, sample sizes for testing, accuracy values, or independent review. This is a specific downstream application mentioned within the broader inventory submission, rather than a claim that the data infrastructure itself proves social benefit.",
            "replication": {
              "produced_by": [
                "Open Data Labs supplied consented behavioral datasets, and the unnamed retailer developed and tested the model internally."
              ],
              "must_hold": [
                "The model, testing population, baseline methods, and accuracy results must be disclosed before the reported improvement can be independently checked."
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              "echoes": []
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        },
        "provenance": {
          "kind": "curatorial",
          "basis": "Mapped from the new case record; this field has not received a separate field-level assessment.",
          "record_fields": [
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            "replication"
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          "source_refs": []
        },
        "review_state": "record_mapped",
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    },
    "summary": {
      "total": 91,
      "addressed": 91,
      "known": 33,
      "reviewed": 19,
      "reviewed_complete": 18,
      "reviewed_partial": 1,
      "record_mapped": 14,
      "review_not_assessed": 58,
      "not_assessed": 58,
      "complete": false,
      "by_state": {
        "value_present": 33,
        "not_reported": 0,
        "unresolved": 0,
        "not_assessed": 58,
        "not_applicable": 0,
        "withheld": 0,
        "source_unavailable": 0
      }
    }
  }
}