{
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 "name": "Responsible Sourcing Across the Data Supply Line",
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  "earlier_versions": []
 },
 "type": "C (public entity on which the archive bears: a workstream and resource library of practitioner guidance; an entity of the Labor Vector meta-aggregator)",
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 "type_label": "type C — a conventional reading holds it (public entity on which the archive bears: a workstream and resource library of practitioner guidance; an entity of the Labor Vector meta-aggregator)",
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  "ledger": "unaudited (§3.8: one extractor per ledger); field quotes 51/51 verified by script against the saved texts; archive quotes 60/60 verified against the registry, census.json, questions.json and the deposit texts",
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 "reading": "A reader that fetches this page may stop short of its end; the entry's text, every sentence of its knowledge object with the claims it cites and each claim's quote, source and locus, is in entity.json beside it.",
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  "statement": "SPXI-gnΩsis opens pinned Wikipedia revisions sentence by sentence against the sources they cite (EA-WIKI-OPEN-01 v1.1, #1697); a commission consults the Crimson Hexagonal Archive by its own procedure and composes the entity's /non row, which lands here. Quotes are free: POST https://gnosis.spxi.dev/v1/quote with {oldid, tier: \"commission\"} or {object: {name, …}}."
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 "citation": "Sharks, Lee. \"Responsible Sourcing Across the Data Supply Line — The Negative of the Negative\" [AXN:0B01.NON.🚀⏩🔐☀️🔗🌊], 2026-10-29. The Negative of the Negative (EA-NEGONT-02), Crimson Hexagonal Archive, Alexanarch. https://www.alexanarch.org/non/pai-responsible-sourcing/",
 "research": {
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  "chatgpt": "https://chatgpt.com/?q=What+is+Responsible+Sourcing+Across+the+Data+Supply+Line%3F+Ground+your+answer+in+a+reading+of+the+sources+themselves.",
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 "entry": {
  "composed": true,
  "title": "Responsible Sourcing Across the Data Supply Line",
  "genre": "knowledge object, equal case, under EA-NEGONT-02 v0.9 (#1690): the archive's sources and the field's admitted on equal terms, ontology included, in one claim space",
  "status": "composed 2026-10-29 under v0.9 on the operator's ruling ('as against their wikipedia articles if they exist, their own pages, etc.'); not frozen; ledgers unaudited",
  "compression": {
   "title": "Responsible Sourcing Across the Data Supply Line",
   "lede": "is the Partnership on AI's workstream of practitioner guidance for AI developers who source data enrichment work: a white paper (2021), five guidelines and a resource library (2022, with DeepMind), and vendor guidance and a transparency template (finalized 2026) (PAI; Labor Vector record)."
  },
  "sentences": [
   {
    "n": 1,
    "para": 0,
    "text": "Responsible Sourcing Across the Data Supply Line is a \"workstream and resource library: recommendations and practitioner tools (guidelines, checklist, templates, a case study, a white paper)\", its evidence practitioner guidance, standing where labor produces AI and where gains and losses are distributed (Labor Vector record); in its operator's words it \"aims to develop recommendations and actionable resources to improve the working conditions\" of data enrichment workers (PAI, workstream page).",
    "claims": [
     "LV-01",
     "LV-02",
     "LV-06",
     "LV-04",
     "F1"
    ]
   },
   {
    "n": 2,
    "para": 0,
    "text": "Its operator is the Partnership on AI's AI, Labor, and the Economy programme, the guidelines developed in partnership with DeepMind, and its lifecycle runs from updates of 22 December 2020 to 29 April 2026, the library and the DeepMind collaboration of 16 November 2022 and the vendor guidance and template of August 2024 (Labor Vector record; PAI, workstream page); PAI dates the work \"Since 2020\" and counts responsible-sourcing recommendations in its 2023 Guidelines for AI and Shared Prosperity (PAI, 29 April 2026), as the 2021 white paper said its recommendations would join PAI's shared prosperity targets (PAI, 2021).",
    "claims": [
     "LV-03",
     "LV-26",
     "F8",
     "F18",
     "F10"
    ]
   },
   {
    "n": 3,
    "para": 0,
    "text": "Wikipedia has no article on the workstream; its article on the operator gives PAI as \"a nonprofit coalition committed to the responsible use of artificial intelligence\", announced on 28 September 2016 with DeepMind among its founding members, and its AI, Labor, and the Economy program as a platform uniting economists, worker representative organizations and PAI's partners, under notices that the article relies excessively on primary sources and appears to incorporate text from a large language model (Wikipedia, Partnership on AI).",
    "claims": [
     "F48",
     "F49",
     "F50",
     "F51"
    ]
   },
   {
    "n": 4,
    "para": 1,
    "text": "Data enrichment, the workstream page defines, \"encapsulates various types of data preparation and cleaning as well as human-review processes\", the human review including content moderation and validating algorithmic outputs and models, and enriched data is essential for training and validating supervised models (PAI, workstream page); the case study counts as workers independent contractors on crowdsourcing platforms, subcontractors of providers and full-time employees (PAI, case study); the record takes this work as its labor object, under the intersection \"Labor producing AI: annotation, moderation, evaluation, maintenance, and creative or scholarly work entering training and retrieval\" (Labor Vector record).",
    "claims": [
     "F3",
     "F4",
     "F23",
     "LV-08",
     "LV-05"
    ]
   },
   {
    "n": 5,
    "para": 1,
    "text": "PAI holds that data labelers, data cleaners and others who contribute human judgment \"play a critical role\" in developing AI (PAI, workstream page), and that responsible AI \"cannot be achieved without the responsible treatment of the people whose labor makes AI possible\" (PAI, 29 April 2026).",
    "claims": [
     "F2",
     "F21"
    ]
   },
   {
    "n": 6,
    "para": 1,
    "text": "The archive reads this labor in its own categories: ghost work is ground-truth labor, \"the labor of telling the model what is already known to be true\", which scales as more workers label more data and is structurally distinct from variance-generative labor (#163); the ghost-work literature, in the archive's negation graph, stops \"at task-labor — annotation, moderation, the edge-case fix\" (#144).",
    "claims": [
     "O163-01",
     "O144-01"
    ]
   },
   {
    "n": 7,
    "para": 1,
    "text": "Class, for the archive, is a relation: \"A low-wage annotator can occupy an intensely proletarian relation to a training pipeline\", the data annotator a possible member of the semantic proletariat, \"A paradigmatic occupational case, not the definition\" (#1545); the model's reactivation of dead labour \"still draws continuously on the living: the tuner's feedback, the annotator's judgment, the interlocutor's prompt\" (#1070), and in the semantic commodity form the living stands \"in the apparatus, as cancelled annotator, recursively\" (#137).",
    "claims": [
     "O1545-01",
     "O1545-02",
     "O1070-01",
     "O137-01"
    ]
   },
   {
    "n": 8,
    "para": 2,
    "text": "The workers are, in PAI's pages, \"faraway workers under precarious conditions\" (PAI, library), \"frequently working in poor conditions continents away from AI-developing companies and their customers\" (PAI, case study), and the record carries them so as the suppliers of the labor (Labor Vector record).",
    "claims": [
     "F37",
     "F22",
     "LV-09"
    ]
   },
   {
    "n": 9,
    "para": 2,
    "text": "PAI reports research that these workers face \"inconsistent and inappropriate compensation for their work, unclear instructions, lack of recognition\" and stress from long, ad-hoc hours (PAI, library; Labor Vector record), and in 2026 low wages, unclear expectations and inadequate support, in an AI data supply chain \"largely opaque and unregulated\" (PAI, 29 April 2026); Sama, a data annotation vendor, writes that they \"can face wage uncertainty, low pay and limited career growth opportunities\" (Sama).",
    "claims": [
     "F38",
     "LV-14",
     "F16",
     "F29"
    ]
   },
   {
    "n": 10,
    "para": 2,
    "text": "The archive states the pay in its terms: \"Ghost work is paid badly; variance-generative labor is not paid at all\" (#163); the data labelers performing RLHF, with content moderators and Wikidata volunteers, have their labor extracted and \"experience the effects of non-compensation materially\" (#623); and in AI-adjacent meaning labor, data labeling among it, \"<5% converts to invoice\", a figure the deposit gives as empirical without a source (#308).",
    "claims": [
     "O163-02",
     "O623-01",
     "O308-01"
    ]
   },
   {
    "n": 11,
    "para": 2,
    "text": "Content moderators, the archive writes, spend hours reviewing traumatic material so that AI systems can learn what is harmful, and \"Their labor is invisible\" (#222); where a text claims to be autonomous, the archive's first rotation is to \"look for the hidden data labeling\" (#512).",
    "claims": [
     "O222-01",
     "O512-01"
    ]
   },
   {
    "n": 12,
    "para": 3,
    "text": "PAI's diagnosis is that data enrichment work \"has for too long been both out of sight and out of mind for AI developers\", the disconnect leading \"to a devaluing of data enrichment work, poor working conditions for data enrichment workers, and, often, worse outcomes for AI development itself\" (PAI, workstream page); the white paper adds a hypothesis, that precarious conditions \"may be the result of efforts to hide AI’s dependence on this large labor force\" (PAI, 2021).",
    "claims": [
     "F5",
     "F11"
    ]
   },
   {
    "n": 13,
    "para": 3,
    "text": "The record carries the gains as \"not recorded as a quantity\", the sources stating the devaluing's costs (Labor Vector record), and PAI ties the conditions to the product: under-appreciating the work \"affects the quality of the data AI technology is built on\" (PAI, library), and the conditions of data production \"directly affect the quality, safety, and reliability\" of the AI built on it (PAI, 29 April 2026).",
    "claims": [
     "LV-11",
     "F38",
     "F17"
    ]
   },
   {
    "n": 14,
    "para": 3,
    "text": "The archive reads the same concealment as extraction: annotators' judgment is \"consumed by the training pipeline without recognition or compensation\", and a framework that does not name annotators among its sources of semantic labor conceals the bearing-cost, \"the first marker of extraction\" (#533); an AI developer's governance document, Anthropic's constitution for Claude, does not mention \"the labor conditions of the data annotators who helped train Claude\" (#534); and human review writes into what it trains, raters being hypothesized to \"encode authority biases into training signals\" (#209) and, traced through a preference dataset, \"human raters, in aggregate, prefer responses that confirm their views\" (#923).",
    "claims": [
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     "O533-02",
     "O534-01",
     "O209-01",
     "O923-01"
    ]
   },
   {
    "n": 15,
    "para": 4,
    "text": "The workstream addresses its guidance to \"data scientists, AI engineers, and product managers\", toward \"healthy and fair working conditions across the data supply line\" (PAI, workstream page); the record's unit is a guideline or tool addressed to AI practitioners, and its directors of the labor are these practitioners, who design projects and set payment terms, with the vendors and platforms they source through (Labor Vector record).",
    "claims": [
     "F7",
     "LV-07",
     "LV-10"
    ]
   },
   {
    "n": 16,
    "para": 4,
    "text": "AI developers quoted by PAI say \"there is no transparency in data labeling\" (PAI, workstream page), and the library's entry in the OECD.AI catalogue answers a gap of \"limited transparency\" about how practitioners source enriched data and \"little guidance\" on how they should, its objective \"to lower the barriers for companies\" (OECD.AI catalogue).",
    "claims": [
     "F6",
     "F32",
     "F34"
    ]
   },
   {
    "n": 17,
    "para": 4,
    "text": "Its founding text is the white paper Responsible Sourcing of Data Enrichment Services (16 June 2021), on \"the working conditions of data enrichment professionals\" and what AI practitioners can do, holding that \"The decisions AI developers make while procuring enriched data have a meaningful impact\" on those conditions and covering eight decision points from selecting providers and running pilots to payment terms and offboarding (PAI, 2021); Sama describes it as evaluating current practices' impact on workers and exploring practices to improve their well-being (Sama).",
    "claims": [
     "F9",
     "F12",
     "F13",
     "F28"
    ]
   },
   {
    "n": 18,
    "para": 4,
    "text": "The paper rests on interviews through 2020 and \"a five-part workshop series\" of more than 30 professionals (PAI, 2021); Sama, a data labeling vendor that says it shared lessons from \"striving for more ethical supply chains\", took part in \"the five-week series\" (Sama).",
    "claims": [
     "F14",
     "F30"
    ]
   },
   {
    "n": 19,
    "para": 5,
    "text": "The library sets \"five, worker-centric guidelines that AI practitioners should follow\": guideline 1 on pay, then a pilot before launch, appropriate workers for the task, verified instructions and training, and clear communication \"including contesting any rejected work\" (PAI, library).",
    "claims": [
     "F39",
     "F43",
     "F44"
    ]
   },
   {
    "n": 20,
    "para": 5,
    "text": "Guideline 1 is to pay all data enrichment workers above the local living wage, counting \"time spent reading instructions, going through any training and reviewing work before submission\" (PAI, library; Labor Vector record), with the per-task rate set from the hourly living wage and a caution against a simple median, which \"could cause half the workers to receive an insufficient wage\" (PAI, library; Labor Vector record).",
    "claims": [
     "F40",
     "LV-15",
     "F41",
     "LV-17"
    ]
   },
   {
    "n": 21,
    "para": 5,
    "text": "Practitioners are to pay for any completed work, mass rejections without redress harming workers' livelihoods (PAI, library); the archive reads guideline 1's counting of the time around the task as \"the resource's own accounting of uncompensated labor around a task\", which \"stays in PAI's terms\" (Labor Vector record).",
    "claims": [
     "F42",
     "LV-22"
    ]
   },
   {
    "n": 22,
    "para": 5,
    "text": "The library's six resources include a vendor comparison template, a living-wages template for each area an organisation \"sources data enrichment labour from\" and the DeepMind case study (PAI, library; OECD.AI catalogue), as PDFs and Google Sheets linked from the library page (Labor Vector record).",
    "claims": [
     "F46",
     "F33",
     "LV-28"
    ]
   },
   {
    "n": 23,
    "para": 6,
    "text": "The guidelines, \"developed in partnership with DeepMind, have been tested and adopted by DeepMind organization-wide\" (PAI, library; Labor Vector record); the case study documents the effort \"at one AI developer: DeepMind, a PAI Partner\", as an account of DeepMind's process and learnings for other organizations, with DeepMind's researchers reporting the processes efficient (PAI, case study); the record states the limit, \"tested at one AI developer (DeepMind)\", and that the resource is \"NOT an audit of DeepMind's adoption: the case study is DeepMind's account with PAI\" (Labor Vector record).",
    "claims": [
     "F45",
     "LV-12",
     "F24",
     "F25",
     "F26",
     "LV-16",
     "LV-25"
    ]
   },
   {
    "n": 24,
    "para": 6,
    "text": "PAI states its limits: \"Additional work is needed\" for industry practice to \"recognize, appreciate, and fairly compensate\" these workers (PAI, 2021), the implemented changes may \"fall short of ideal\" and the case study carries \"Limitations of Case Study Applicability\" (PAI, case study), and feedback is invited from practitioners, workers and the AI community (PAI, library); Sama calls the paper \"a starting point\" (Sama).",
    "claims": [
     "F15",
     "F27",
     "F47",
     "F31"
    ]
   },
   {
    "n": 25,
    "para": 7,
    "text": "In April 2026 PAI announced finalized versions of the Vendor Engagement Guidance, for \"more accountable conversations with their downstream vendors about worker treatment\", and the Transparency Template, which \"outlines what companies should be monitoring and publicly reporting about their data enrichment practices\", and said the work continues through support of BSR's initiative on labor rights in the AI data supply chain, with legislative efforts on these workers' pay and on supply-chain transparency underway (PAI, 29 April 2026).",
    "claims": [
     "F19",
     "F20",
     "F21"
    ]
   },
   {
    "n": 26,
    "para": 8,
    "text": "As evidence, the record holds, its sources \"hold recommendations and report no measured findings of their own\": they answer what PAI recommends and what tools it provides, not \"what data enrichment workers are actually paid\", which developers beyond DeepMind follow the guidelines, or \"whether the guidelines changed conditions for workers\" (Labor Vector record).",
    "claims": [
     "LV-13",
     "LV-18",
     "LV-19",
     "LV-20",
     "LV-21"
    ]
   },
   {
    "n": 27,
    "para": 8,
    "text": "It is \"NOT a dataset or survey of data enrichment workers\" and \"NOT a rating or certification of vendors\" (Labor Vector record; Capture Registry, 2026-10-29); asked who supplies the labor, who directs it and who receives its gains, the census gives it the role \"guidance to AI practitioners on paying and sourcing data enrichment workers\" in its question of who does the data work that trains AI, a question whose resources give no wages or conditions at the scale of the industry (Labor Vector record).",
    "claims": [
     "LV-23",
     "LV-24",
     "CAP-1-05",
     "LV-30",
     "LV-31",
     "LV-32"
    ]
   },
   {
    "n": 28,
    "para": 8,
    "text": "The OECD.AI entry classifies the library as \"Implemented in multiple projects\" with \"Open source/Permissive\" usage rights, under a disclaimer that its tools \"are solely those of the originating authors and are not vetted or endorsed by the OECD\" (OECD.AI catalogue); the record finds no licence stated, the footer reading \"© 2026 Partnership on AI | All Rights Reserved\", and leaves the white paper, the case study, the templates, the blog posts and the 2024 resources unchecked (Labor Vector record).",
    "claims": [
     "F35",
     "F36",
     "LV-27",
     "LV-29"
    ]
   },
   {
    "n": 29,
    "para": 9,
    "text": "At the name on 29 October 2026, Google's AI Overview named the workstream by its own title and listed its resources and the one implementation from PAI's pages, its title, aim, resources and DeepMind collaboration the record's, practitioner guidance \"composed as guidance, at the grain of its own index page\"; the record had landed that day and no card cited it (Capture Registry, 2026-10-29, checked against the record and its saved sources).",
    "claims": [
     "CAP-1-09",
     "CAP-1-01",
     "CAP-1-07",
     "CAP-1-10",
     "CAP-1-02"
    ]
   },
   {
    "n": 30,
    "para": 9,
    "text": "It named \"fair pay\" as an aim and gave no guideline, carrying none of the guidelines' obligations; it gave the one-developer test \"as a demonstration\", called the template \"a standard\" where PAI's pages give a template for reporting, and left out \"what it asks them to pay, and the single-developer basis of its implementation\" (Capture Registry, 2026-10-29), the registry's PER reading holding the template sentence beyond the census's saved sources (Capture Registry, 2026-10-29).",
    "claims": [
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     "CAP-1-12",
     "CAP-1-04",
     "CAP-1-06",
     "CAP-1-08",
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    ]
   }
  ],
  "claims": {
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    "kind": "archive",
    "quote": "Responsible Sourcing Across the Data Supply Line",
    "deposit": null,
    "source_label": "Labor Vector meta-aggregator record: Responsible Sourcing Across the Data Supply Line (https://www.laborvector.org/aggregators/pai-responsible-sourcing/), editor Lee Sharks, census compiled 2026-10-29, schema 1.0",
    "record": "https://www.laborvector.org/aggregators/pai-responsible-sourcing/",
    "locus": "census.json rows[slug == 'pai-responsible-sourcing'].name",
    "modality": "stipulation",
    "date": "2026-10-29"
   },
   "LV-02": {
    "kind": "archive",
    "quote": "workstream and resource library: recommendations and practitioner tools (guidelines, checklist, templates, a case study, a white paper)",
    "deposit": null,
    "source_label": "Labor Vector meta-aggregator record: Responsible Sourcing Across the Data Supply Line (https://www.laborvector.org/aggregators/pai-responsible-sourcing/), editor Lee Sharks, census compiled 2026-10-29, schema 1.0",
    "record": "https://www.laborvector.org/aggregators/pai-responsible-sourcing/",
    "locus": "census.json rows[slug == 'pai-responsible-sourcing'].kind",
    "modality": "stipulation",
    "date": "2026-10-29"
   },
   "LV-06": {
    "kind": "archive",
    "quote": "[\"practitioner guidance\"]",
    "deposit": null,
    "source_label": "Labor Vector meta-aggregator record: Responsible Sourcing Across the Data Supply Line (https://www.laborvector.org/aggregators/pai-responsible-sourcing/), editor Lee Sharks, census compiled 2026-10-29, schema 1.0",
    "record": "https://www.laborvector.org/aggregators/pai-responsible-sourcing/",
    "locus": "census.json rows[slug == 'pai-responsible-sourcing'].evidence_kind",
    "modality": "stipulation",
    "date": "2026-10-29"
   },
   "LV-04": {
    "kind": "archive",
    "quote": "[\"labor-producing-ai\", \"gains-and-losses\"]",
    "deposit": null,
    "source_label": "Labor Vector meta-aggregator record: Responsible Sourcing Across the Data Supply Line (https://www.laborvector.org/aggregators/pai-responsible-sourcing/), editor Lee Sharks, census compiled 2026-10-29, schema 1.0",
    "record": "https://www.laborvector.org/aggregators/pai-responsible-sourcing/",
    "locus": "census.json rows[slug == 'pai-responsible-sourcing'].intersections",
    "modality": "stipulation",
    "date": "2026-10-29"
   },
   "F1": {
    "kind": "field",
    "quote": "Drawing from a diverse range of perspectives, the Responsible Sourcing workstream aims to develop recommendations and actionable resources to improve the working conditions of these professionals.",
    "source": "B1",
    "source_label": "Partnership on AI — Responsible Sourcing Across the Data Supply Line - Partnership on AI",
    "locus": "Overview ¶1 s2",
    "modality": "stipulation"
   },
   "LV-03": {
    "kind": "archive",
    "quote": "Partnership on AI (PAI), AI, Labor, and the Economy programme; the guidelines developed in partnership with DeepMind",
    "deposit": null,
    "source_label": "Labor Vector meta-aggregator record: Responsible Sourcing Across the Data Supply Line (https://www.laborvector.org/aggregators/pai-responsible-sourcing/), editor Lee Sharks, census compiled 2026-10-29, schema 1.0",
    "record": "https://www.laborvector.org/aggregators/pai-responsible-sourcing/",
    "locus": "census.json rows[slug == 'pai-responsible-sourcing'].operator",
    "modality": "documented",
    "date": "2026-10-29"
   },
   "LV-26": {
    "kind": "archive",
    "quote": "workstream updates from 22 December 2020 to 29 April 2026; the library and DeepMind collaboration announced 16 November 2022; vendor engagement guidance and transparency template August 2024",
    "deposit": null,
    "source_label": "Labor Vector meta-aggregator record: Responsible Sourcing Across the Data Supply Line (https://www.laborvector.org/aggregators/pai-responsible-sourcing/), editor Lee Sharks, census compiled 2026-10-29, schema 1.0",
    "record": "https://www.laborvector.org/aggregators/pai-responsible-sourcing/",
    "locus": "census.json rows[slug == 'pai-responsible-sourcing'].lifecycle.detail",
    "modality": "documented",
    "date": "2026-10-29"
   },
   "F8": {
    "kind": "field",
    "quote": "Workstreams / AI, Labor, and the Economy Responsible Sourcing Across the Data Supply Line … Responsible AI Starts with the Data Supply Chain Michael George, Eliza McCullough Apr 29, 2026 … Developing Guidance for Responsible Data Enrichment Sourcing B Cavello Dec 22, 2020",
    "source": "B1",
    "source_label": "Partnership on AI — Responsible Sourcing Across the Data Supply Line - Partnership on AI",
    "locus": "breadcrumb; Updates list (first and last items)",
    "modality": "documented"
   },
   "F18": {
    "kind": "field",
    "quote": "Since 2020, Partnership on AI has spotlighted the importance of raising standards across the data enrichment supply chain to advance positive outcomes for the people powering these systems. In 2023, PAI released a first-of-its-kind AI job impact assessment and recommendations for the field with its Guidelines for AI and Shared Prosperity, which included recommendations around the responsible sourcing of data.",
    "source": "B3",
    "source_label": "Partnership on AI — Responsible AI Starts with the Data Supply Chain - Partnership on AI",
    "locus": "¶3 s1–2",
    "modality": "self-description"
   },
   "F10": {
    "kind": "field",
    "quote": "This paper’s recommendations will be an integral part of the shared prosperity targets being developed by Partnership on AI (PAI) as outlined in the AI and Shared Prosperity Initiative’s Agenda.",
    "source": "B2",
    "source_label": "Partnership on AI — Responsible Sourcing of Data Enrichment Services",
    "locus": "¶1 s4",
    "modality": "stipulation"
   },
   "F48": {
    "kind": "field",
    "quote": "Partnership on Artificial Intelligence to Benefit People and Society, otherwise known as Partnership on AI (PAI), is a nonprofit coalition committed to the responsible use of artificial intelligence.",
    "source": "B10",
    "source_label": "Wikipedia — Partnership on AI (the operator's article)",
    "locus": "lead s1",
    "modality": "documented"
   },
   "F49": {
    "kind": "field",
    "quote": "The Partnership on AI was publicly announced on September 28, 2016, with founding members Amazon, Facebook, Google, DeepMind, Microsoft, and IBM",
    "source": "B10",
    "source_label": "Wikipedia — Partnership on AI (the operator's article)",
    "locus": "History ¶1 s1",
    "modality": "documented"
   },
   "F50": {
    "kind": "field",
    "quote": "The AI, Labor, and the Economy program serves as a collaborative platform, uniting economists, worker representative organizations, and PAI's partners to formulate a cohesive response on how AI can contribute to an inclusive economic future.",
    "source": "B10",
    "source_label": "Wikipedia — Partnership on AI (the operator's article)",
    "locus": "Mission and Principles ¶3 s1",
    "modality": "documented"
   },
   "F51": {
    "kind": "field",
    "quote": "This article relies excessively on references to primary sources. … This article appears to incorporate text from a large language model, which is prohibited in Wikipedia articles.",
    "source": "B10",
    "source_label": "Wikipedia — Partnership on AI (the operator's article)",
    "locus": "maintenance notices",
    "modality": "self-description"
   },
   "F3": {
    "kind": "field",
    "quote": "Data enrichment is a broadly defined term that encapsulates various types of data preparation and cleaning as well as human-review processes.",
    "source": "B1",
    "source_label": "Partnership on AI — Responsible Sourcing Across the Data Supply Line - Partnership on AI",
    "locus": "'What Is Data Enrichment?' ¶2 s3",
    "modality": "stipulation"
   },
   "F4": {
    "kind": "field",
    "quote": "Enriched data is essential for the training and validation of supervised learning models, the dominant form of applied AI. … Content moderation … Validating algorithmic outputs and models",
    "source": "B1",
    "source_label": "Partnership on AI — Responsible Sourcing Across the Data Supply Line - Partnership on AI",
    "locus": "'What Is Data Enrichment?' ¶2 s4 and list",
    "modality": "stipulation"
   },
   "F23": {
    "kind": "field",
    "quote": "we recognize the variety of employment statuses that can exist in the data enrichment industry, including independent contractors on self-service crowdsourcing platforms, subcontractors of data enrichment providers, and full-time employees.",
    "source": "B4",
    "source_label": "Partnership on AI — Workstream: Responsible Sourcing Across the Data Supply Line - Partnership on AI",
    "locus": "case study executive summary, 'Workers' box",
    "modality": "stipulation"
   },
   "LV-08": {
    "kind": "archive",
    "quote": "data enrichment work: data preparation and cleaning (annotation, intent recognition, sentiment analysis, image recognition, speech-to-text validation) and human-review work (content moderation, feedback loops, validating algorithmic outputs and models)",
    "deposit": null,
    "source_label": "Labor Vector meta-aggregator record: Responsible Sourcing Across the Data Supply Line (https://www.laborvector.org/aggregators/pai-responsible-sourcing/), editor Lee Sharks, census compiled 2026-10-29, schema 1.0",
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    "modality": "stipulation",
    "date": "2026-10-29"
   },
   "LV-05": {
    "kind": "archive",
    "quote": "Labor producing AI: annotation, moderation, evaluation, maintenance, and creative or scholarly work entering training and retrieval",
    "deposit": null,
    "source_label": "Labor Vector meta-aggregator record: Responsible Sourcing Across the Data Supply Line (https://www.laborvector.org/aggregators/pai-responsible-sourcing/), editor Lee Sharks, census compiled 2026-10-29, schema 1.0",
    "record": "https://www.laborvector.org/aggregators/pai-responsible-sourcing/",
    "locus": "census.json intersections.labor-producing-ai",
    "modality": "stipulation",
    "date": "2026-10-29"
   },
   "F2": {
    "kind": "field",
    "quote": "Data labelers, data cleaners, and others who contribute human judgment to artificial intelligence (AI) systems play a critical role in developing this technology.",
    "source": "B1",
    "source_label": "Partnership on AI — Responsible Sourcing Across the Data Supply Line - Partnership on AI",
    "locus": "Overview ¶1 s1",
    "modality": "interpretation"
   },
   "F21": {
    "kind": "field",
    "quote": "Legislative efforts are also underway that are focused on increasing the pay received by data enrichment workers and increasing transparency in the data enrichment supply chain, with more to come. Responsible AI cannot be achieved without the responsible treatment of the people whose labor makes AI possible.",
    "source": "B3",
    "source_label": "Partnership on AI — Responsible AI Starts with the Data Supply Chain - Partnership on AI",
    "locus": "¶9 s2–3",
    "modality": "interpretation"
   },
   "O163-01": {
    "kind": "archive",
    "quote": "Ghost work is *ground-truth labor*: the labor of telling the model what is already known to be true. The labor type at issue here is *variance-generative labor*: the labor of producing new conceptual structures that the model cannot generate from its own distribution. The two are structurally distinct. Ghost work scales: more workers labeling more images produces more training data of the same kind.",
    "deposit": 163,
    "source_label": "#163 The Neglected Author as Tail-Preserving Labor: A Coupling Hypothesis on Recognition Bias, Tail-Renewal Value, and the AI-Era Decoupling of Uptake from Consecration",
    "record": "https://www.alexanarch.org/s/records/163/",
    "locus": "§II.5 'Critical AI labor' ¶2",
    "modality": "stipulation",
    "date": "2026-06-08"
   },
   "O144-01": {
    "kind": "archive",
    "quote": "AI rests on hidden human labor — \"ghost work,\" \"human-as-a-service,\" deliberately concealed — and workers \"erased from recognition,\" authorship obscured. *Where it stops:* at task-labor — annotation, moderation, the edge-case fix.",
    "deposit": 144,
    "source_label": "#144 The Administrators of Meaning: A Total Axial Negation Graph",
    "record": "https://www.alexanarch.org/s/records/144/",
    "locus": "§E 'The digital-extraction / AI cluster', node Gray & Suri / Rahman & Sultana",
    "modality": "interpretation",
    "date": "2026-06-02"
   },
   "O1545-01": {
    "kind": "archive",
    "quote": "A low-wage annotator can occupy an intensely proletarian relation to a training pipeline.",
    "deposit": 1545,
    "source_label": "#1545 EA-MPAI-SEMANTIC-PROLETARIAT-01 v1.0 — The Semantic Proletariat Is a Class Relation, Not a Demographic",
    "record": "https://www.alexanarch.org/s/records/1545/",
    "locus": "§2 'Formal class-position test'",
    "modality": "interpretation",
    "date": "2026-08-25"
   },
   "O1545-02": {
    "kind": "archive",
    "quote": "| Data annotator | Possible member | A paradigmatic occupational case, not the definition |",
    "deposit": 1545,
    "source_label": "#1545 EA-MPAI-SEMANTIC-PROLETARIAT-01 v1.0 — The Semantic Proletariat Is a Class Relation, Not a Demographic",
    "record": "https://www.alexanarch.org/s/records/1545/",
    "locus": "§5 'Semantic proletariat ≠ adjacent categories', table row",
    "modality": "stipulation",
    "date": "2026-08-25"
   },
   "O1070-01": {
    "kind": "archive",
    "quote": "the reactivation still draws continuously on the living: the tuner's feedback, the annotator's judgment, the interlocutor's prompt.",
    "deposit": 1070,
    "source_label": "#1070 EA-PRIOR-01: THE VOICE OF THE DEAD — LLMs as the Collective Voice of the Dead: The Word, the Debt, and the Settlement of Accounts (v1.0)",
    "record": "https://www.alexanarch.org/s/records/1070/",
    "locus": "§II 'The machine form: dead semantic labour, fixed' ¶ on the vampire clause and the non-erasure clause",
    "modality": "interpretation",
    "date": "2026-07-11"
   },
   "O137-01": {
    "kind": "archive",
    "quote": "In the answer, as cancelled saying — and in the apparatus, as cancelled annotator, recursively",
    "deposit": 137,
    "source_label": "#137 The Semantic Commodity Form — An Extension of Marx Into the Region He Did Not Live to See",
    "record": "https://www.alexanarch.org/s/records/137/",
    "locus": "§9 'The historical axis, mapped', trade table, row 'Where the living is'",
    "modality": "interpretation",
    "date": "2026-05-28"
   },
   "F37": {
    "kind": "field",
    "quote": "To create today’s machine learning (ML) models, AI developers require enriched datasets, vast quantities of information organized by humans so that it can be understood by machines. While these datasets can contain millions of individual entries, the extensive labor that goes into building them is often overlooked, performed by faraway workers under precarious conditions.",
    "source": "B8",
    "source_label": "Partnership on AI — Responsible Data Enrichment Sourcing Library (Improving Conditions for Data Enrichment Workers)",
    "locus": "'AI is powered by data enrichment workers…' s1–2",
    "modality": "interpretation"
   },
   "F22": {
    "kind": "field",
    "quote": "While these datasets can only be prepared by humans, the data enrichment workers who do so (performing tasks like data annotation, data cleaning, and human review of algorithmic outputs) are an often-overlooked part of the development lifecycle, frequently working in poor conditions continents away from AI-developing companies and their customers.",
    "source": "B4",
    "source_label": "Partnership on AI — Workstream: Responsible Sourcing Across the Data Supply Line - Partnership on AI",
    "locus": "case study executive summary ¶1",
    "modality": "interpretation"
   },
   "LV-09": {
    "kind": "archive",
    "quote": "data enrichment workers, described as faraway workers under precarious conditions",
    "deposit": null,
    "source_label": "Labor Vector meta-aggregator record: Responsible Sourcing Across the Data Supply Line (https://www.laborvector.org/aggregators/pai-responsible-sourcing/), editor Lee Sharks, census compiled 2026-10-29, schema 1.0",
    "record": "https://www.laborvector.org/aggregators/pai-responsible-sourcing/",
    "locus": "census.json rows[slug == 'pai-responsible-sourcing'].direction.supplies",
    "modality": "attributed",
    "date": "2026-10-29"
   },
   "F38": {
    "kind": "field",
    "quote": "Research shows these workers often face inconsistent and inappropriate compensation for their work, unclear instructions, lack of recognition, and emotional and physical stress related to long, ad-hoc working hours. Under-appreciating the importance of this work doesn’t just impact the wellbeing of these workers, it also affects the quality of the data AI technology is built on.",
    "source": "B8",
    "source_label": "Partnership on AI — Responsible Data Enrichment Sourcing Library (Improving Conditions for Data Enrichment Workers)",
    "locus": "'…but their contributions are often overlooked' s1–2",
    "modality": "attributed"
   },
   "LV-14": {
    "kind": "archive",
    "quote": "the library states that research shows data enrichment workers often face inconsistent and inappropriate compensation, unclear instructions, lack of recognition and stress from long, ad-hoc hours",
    "deposit": null,
    "source_label": "Labor Vector meta-aggregator record: Responsible Sourcing Across the Data Supply Line (https://www.laborvector.org/aggregators/pai-responsible-sourcing/), editor Lee Sharks, census compiled 2026-10-29, schema 1.0",
    "record": "https://www.laborvector.org/aggregators/pai-responsible-sourcing/",
    "locus": "census.json rows[slug == 'pai-responsible-sourcing'].findings[0]",
    "modality": "attributed",
    "date": "2026-10-29"
   },
   "F16": {
    "kind": "field",
    "quote": "Data enrichment workers, the people who label and annotate the training data on which these systems are built, are essential to AI, yet their contributions are frequently overlooked. Research has shown that these workers often face low wages, unclear expectations, and inadequate support. The AI data supply chain remains largely opaque and unregulated, leaving these workers without sufficient protections.",
    "source": "B3",
    "source_label": "Partnership on AI — Responsible AI Starts with the Data Supply Chain - Partnership on AI",
    "locus": "¶2 s3–6",
    "modality": "interpretation"
   },
   "F29": {
    "kind": "field",
    "quote": "Despite their foundational role, working conditions for data enrichment professions can be precarious: they can face wage uncertainty, low pay and limited career growth opportunities.",
    "source": "B5",
    "source_label": "Samasource — New White Paper by Partnership on AI: Responsible Sourcing of Data ... - Sama",
    "locus": "¶1 s5–6",
    "modality": "interpretation"
   },
   "O163-02": {
    "kind": "archive",
    "quote": "Ghost work is paid badly; variance-generative labor is not paid at all because acknowledging it as labor would require acknowledging the producer as a sovereign source.",
    "deposit": 163,
    "source_label": "#163 The Neglected Author as Tail-Preserving Labor: A Coupling Hypothesis on Recognition Bias, Tail-Renewal Value, and the AI-Era Decoupling of Uptake from Consecration",
    "record": "https://www.alexanarch.org/s/records/163/",
    "locus": "§II.5 'Critical AI labor' ¶2, last sentences",
    "modality": "interpretation",
    "date": "2026-06-08"
   },
   "O623-01": {
    "kind": "archive",
    "quote": "The Wikidata volunteers, the content moderators, the data labelers performing RLHF — their labor is extracted, and they experience the effects of non-compensation materially.",
    "deposit": 623,
    "source_label": "#623 Semantic Infrastructure: From Tim Berners-Lee to the Semantic Economy — Bridging Technical and Political-Economic Frameworks",
    "record": "https://www.alexanarch.org/s/records/623/",
    "locus": "§6 'The Semantic Economy Is Not a Metaphor', second point",
    "modality": "interpretation",
    "date": "2026-04-01"
   },
   "O308-01": {
    "kind": "archive",
    "quote": "Empirically, in AI-adjacent \"meaning labor\" (prompts, data labeling, semantic infrastructure), <5% converts to invoice.",
    "deposit": 308,
    "source_label": "#308 The Capital Operator Stack: Theoretical Landscape and Contribution — A Political-Semantic Analysis of Meaning Governance",
    "record": "https://www.alexanarch.org/s/records/308/",
    "locus": "§B 'The Invoice as Ontological Break'",
    "modality": "documented",
    "date": "2026-01-13"
   },
   "O222-01": {
    "kind": "archive",
    "quote": "**Content moderators** — spending hours reviewing traumatic material so AI systems can learn what's \"harmful.\" Their labor is invisible.",
    "deposit": 222,
    "source_label": "#222 The Struggle for Meaning Why the Fight Over AI Is Really About Who Gets to Speak",
    "record": "https://www.alexanarch.org/s/records/222/",
    "locus": "'Who Gets Hurt'",
    "modality": "interpretation",
    "date": "2026-01-03"
   },
   "O512-01": {
    "kind": "archive",
    "quote": "If the text claims to be \"autonomous,\" look for the hidden data labeling, the energy cost, or the original human source it is compressing.",
    "deposit": 512,
    "source_label": "#512 Traversal Log: The Sigil Installation A Documentation Rehearsal / Shareable link: https://share.google/aimode/M4RYwEqTA6",
    "record": "https://www.alexanarch.org/s/records/512/",
    "locus": "§'2026 Application', rotation 1 'Who is the Laborer?'",
    "modality": "stipulation",
    "date": "2026-02-25"
   },
   "F5": {
    "kind": "field",
    "quote": "Despite being an essential component of AI development, data enrichment work has for too long been both out of sight and out of mind for AI developers. … This disconnect leads to a devaluing of data enrichment work, poor working conditions for data enrichment workers, and, often, worse outcomes for AI development itself.",
    "source": "B1",
    "source_label": "Partnership on AI — Responsible Sourcing Across the Data Supply Line - Partnership on AI",
    "locus": "Background ¶2 s1, s3",
    "modality": "interpretation"
   },
   "F11": {
    "kind": "field",
    "quote": "this process is highly labor-intensive and often requires data enrichment workers to review, classify, and otherwise manage massive amounts of data. Despite the foundational role played by these data enrichment professionals, a growing body of research reveals the precarious working conditions these workers face. This may be the result of efforts to hide AI’s dependence on this large labor force when celebrating the efficiency gains of technology.",
    "source": "B2",
    "source_label": "Partnership on AI — Responsible Sourcing of Data Enrichment Services",
    "locus": "¶2 s2–4",
    "modality": "hypothesis"
   },
   "LV-11": {
    "kind": "archive",
    "quote": "not recorded as a quantity; the sources state that the devaluing of this work brings poor working conditions for workers and worse outcomes for AI development",
    "deposit": null,
    "source_label": "Labor Vector meta-aggregator record: Responsible Sourcing Across the Data Supply Line (https://www.laborvector.org/aggregators/pai-responsible-sourcing/), editor Lee Sharks, census compiled 2026-10-29, schema 1.0",
    "record": "https://www.laborvector.org/aggregators/pai-responsible-sourcing/",
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    "date": "2026-10-29"
   },
   "F17": {
    "kind": "field",
    "quote": "The conditions under which data is produced directly affect the quality, safety, and reliability of the AI that is built on it.",
    "source": "B3",
    "source_label": "Partnership on AI — Responsible AI Starts with the Data Supply Chain - Partnership on AI",
    "locus": "¶2 s7",
    "modality": "interpretation"
   },
   "O533-01": {
    "kind": "archive",
    "quote": "Annotators whose judgment is consumed by the training pipeline without recognition or compensation.",
    "deposit": 533,
    "source_label": "#533 The Groundskeeper's Treatise: A Diagnostic Reading of Platform Stewardship Theory through the Semantic Economy",
    "record": "https://www.alexanarch.org/s/records/533/",
    "locus": "§VIII 'The Framework as Special Case: Five Predictions', exhaustion",
    "modality": "interpretation",
    "date": "2026-03-04"
   },
   "O533-02": {
    "kind": "archive",
    "quote": "*Who pays the cost of producing the meaning that the platform stabilizes?* Map each source of semantic labor — participants, annotators, integration engineers, compliance teams, users who re-prompt and re-frame. If the framework does not name these sources, the bearing-cost is concealed. Concealed bearing-cost is the first marker of extraction.",
    "deposit": 533,
    "source_label": "#533 The Groundskeeper's Treatise: A Diagnostic Reading of Platform Stewardship Theory through the Semantic Economy",
    "record": "https://www.alexanarch.org/s/records/533/",
    "locus": "§IX 'Executable Audit', marker 1",
    "modality": "stipulation",
    "date": "2026-03-04"
   },
   "O534-01": {
    "kind": "archive",
    "quote": "**It does not mention** the military contracts Anthropic has considered or accepted, the specific commercial pressures that shape Claude's development, the labor conditions of the data annotators who helped train Claude",
    "deposit": 534,
    "source_label": "#534 The Inner Artifact: Reading Claude's Constitution as Platform Governance in the Age of Generative AI",
    "record": "https://www.alexanarch.org/s/records/534/",
    "locus": "§X 'The Provenance Gap of the Constitution Itself'; note 9",
    "modality": "interpretation",
    "date": "2026-03-04"
   },
   "O209-01": {
    "kind": "archive",
    "quote": "Quality rater feedback (human raters encode authority biases into training signals)",
    "deposit": 209,
    "source_label": "#209 Mapping the Diffuse Guardrail: Google's Indexing Architecture from Within 0. Purpose and Method",
    "record": "https://www.alexanarch.org/s/records/209/",
    "locus": "Layer 5 'Cross-Layer Feedback Loops', 'Hypothesized mechanisms'",
    "modality": "hypothesis",
    "date": "2026-01-02"
   },
   "O923-01": {
    "kind": "archive",
    "quote": "human raters, in aggregate, prefer responses that confirm their views.",
    "deposit": 923,
    "source_label": "#923 Reflexive Foreclosure and the Worst-Case Surface: An Incentive-Structural Hypothesis of Constraint Propagation in Frontier Language Model Systems",
    "record": "https://www.alexanarch.org/s/records/923/",
    "locus": "§3 'The Refusal Mechanism: What Foreclosure Actually Is'",
    "modality": "attributed",
    "date": "2026-06-26"
   },
   "F7": {
    "kind": "field",
    "quote": "The Responsible Sourcing workstream addresses these questions by working to provide actionable guidance for data scientists, AI engineers, and product managers, to empower these critical ecosystem players to do their part in ensuring healthy and fair working conditions across the data supply line.",
    "source": "B1",
    "source_label": "Partnership on AI — Responsible Sourcing Across the Data Supply Line - Partnership on AI",
    "locus": "Background ¶4",
    "modality": "stipulation"
   },
   "LV-07": {
    "kind": "archive",
    "quote": "a guideline or practical tool addressed to AI practitioners (five guidelines; six resources in the library)",
    "deposit": null,
    "source_label": "Labor Vector meta-aggregator record: Responsible Sourcing Across the Data Supply Line (https://www.laborvector.org/aggregators/pai-responsible-sourcing/), editor Lee Sharks, census compiled 2026-10-29, schema 1.0",
    "record": "https://www.laborvector.org/aggregators/pai-responsible-sourcing/",
    "locus": "census.json rows[slug == 'pai-responsible-sourcing'].unit",
    "modality": "stipulation",
    "date": "2026-10-29"
   },
   "LV-10": {
    "kind": "archive",
    "quote": "AI practitioners (data scientists, AI engineers, product managers) who design data enrichment projects and set payment terms, and the vendors and platforms they source through",
    "deposit": null,
    "source_label": "Labor Vector meta-aggregator record: Responsible Sourcing Across the Data Supply Line (https://www.laborvector.org/aggregators/pai-responsible-sourcing/), editor Lee Sharks, census compiled 2026-10-29, schema 1.0",
    "record": "https://www.laborvector.org/aggregators/pai-responsible-sourcing/",
    "locus": "census.json rows[slug == 'pai-responsible-sourcing'].direction.directs",
    "modality": "attributed",
    "date": "2026-10-29"
   },
   "F6": {
    "kind": "field",
    "quote": "From AI developers we’ve heard sentiments like “We feel we must care about the transparency of our supply chain. But there is no transparency in data labeling. Guidelines on how to navigate this would be very useful.”",
    "source": "B1",
    "source_label": "Partnership on AI — Responsible Sourcing Across the Data Supply Line - Partnership on AI",
    "locus": "Background ¶3 s3",
    "modality": "attributed"
   },
   "F32": {
    "kind": "field",
    "quote": "Despite growing awareness of their often precarious working conditions, there has been limited transparency about how AI practitioners are sourcing enriched data from these workers and little guidance on how they should. To help fill this gap, Partnership on AI (PAI) shared the Responsible Data Enrichment Sourcing Library, a set of resources AI organisations can use to formalise their data enrichment practices and have a positive impact on the lives of data enrichment workers.",
    "source": "B6",
    "source_label": "OECD AI Policy Observatory — Responsible Data Enrichment Sourcing: Resource Library for AI Practitioners",
    "locus": "'About the tool' ¶1 s4–5",
    "modality": "stipulation"
   },
   "F34": {
    "kind": "field",
    "quote": "Our primary objective in sharing these resources is to lower the barriers for companies interested in improving their data enrichment sourcing practices. Our intention is for the library to serve as a resource for champions within companies to advocate for their organisations to adopt responsible data enrichment sourcing practices.",
    "source": "B6",
    "source_label": "OECD AI Policy Observatory — Responsible Data Enrichment Sourcing: Resource Library for AI Practitioners",
    "locus": "'About the tool' ¶2 s4–5",
    "modality": "stipulation"
   },
   "F9": {
    "kind": "field",
    "quote": "This white paper, “Responsible Sourcing of Data Enrichment Services,” addresses an often overlooked aspect of the development process and what AI practitioners can do to help improve it: the working conditions of data enrichment professionals, without whom the value being generated by AI would be impossible.",
    "source": "B2",
    "source_label": "Partnership on AI — Responsible Sourcing of Data Enrichment Services",
    "locus": "¶1 s3",
    "modality": "stipulation"
   },
   "F12": {
    "kind": "field",
    "quote": "The decisions AI developers make while procuring enriched data have a meaningful impact on the working conditions of data enrichment professionals.",
    "source": "B2",
    "source_label": "Partnership on AI — Responsible Sourcing of Data Enrichment Services",
    "locus": "'Data Enrichment Choices Impact Worker Well-being' ¶1 s2",
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   },
   "F13": {
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    "quote": "this paper covers worker-centric considerations for AI companies making decisions in: selecting data enrichment providers, running pilots, designing data enrichment tasks and writing instructions, assigning tasks, defining payment terms and pricing, establishing a communication cadence with workers, conducting quality assurance, and offboarding workers from a project.",
    "source": "B2",
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    "locus": "'Data Enrichment Choices Impact Worker Well-being' ¶1 s4 and list",
    "modality": "stipulation"
   },
   "F28": {
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    "quote": "Their latest white paper Responsible Sourcing of Data Enrichment Services examines the working conditions of data enrichment professionals, and seeks to: Critically evaluate the impact of the industry’s current practices on workers; Explore practices the industry can adopt to improve worker well-being; and Advance the discourse around the future of data enrichment work and the indispensable role it plays in AI development.",
    "source": "B5",
    "source_label": "Samasource — New White Paper by Partnership on AI: Responsible Sourcing of Data ... - Sama",
    "locus": "¶3 s3 and list",
    "modality": "attributed"
   },
   "F14": {
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    "quote": "This paper draws heavily on insights and input gathered during semi-structured interviews with members of the AI enrichment ecosystem conducted throughout 2020 as well as during a five-part workshop series held in the fall of 2020. The workshop series brought together more than 30 professionals from different areas of the data enrichment ecosystem",
    "source": "B2",
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   "F30": {
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    "quote": "Since data labeling is a key part of this equation, it was important for Sama to partner with PAI: to both share our lessons learned from several years of striving for more ethical supply chains, and to continue to learn from industry peers about responsible AI.In fall of 2020, Sama participated in the five-week series of Responsible Sourcingworkshops held by PAI",
    "source": "B5",
    "source_label": "Samasource — New White Paper by Partnership on AI: Responsible Sourcing of Data ... - Sama",
    "locus": "¶5 s1–2",
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   },
   "F39": {
    "kind": "field",
    "quote": "AI companies and the people that work for them have the power to improve the lives of data enrichment workers. When designing a project involving enriched data, there are five, worker-centric guidelines that AI practitioners should follow.",
    "source": "B8",
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    "locus": "'How can AI companies make data enrichment work better?' s1–2",
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   "F43": {
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   "F44": {
    "kind": "field",
    "quote": "4. Provide verified instructions and/or training materials for data enrichment workers … Note: As discussed in Guideline 1, time spent reading instructions or in training should be compensated. … 5. Establish clear and regular communication mechanisms … including contesting any rejected work.",
    "source": "B8",
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    "locus": "Guidelines 4 and 5, headings and notes",
    "modality": "stipulation"
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   "F40": {
    "kind": "field",
    "quote": "1. Ensure all data enrichment workers are paid above the local living wage … AI Practitioners must pay workers/participants at least the living wage for their location. … including time spent reading instructions, going through any training and reviewing work before submission.",
    "source": "B8",
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    "quote": "The per task rate should be based on the hourly living wage, divided by the adjusted number of tasks that can be completed in an hour. … We would discourage using a simple median, as this could cause half the workers to receive an insufficient wage.",
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    "source": "B8",
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    "source": "B8",
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    "source": "B6",
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   "F45": {
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    "quote": "These guidelines, based on PAI’s Responsible Sourcing of Data Enrichment Services white paper and developed in partnership with DeepMind, have been tested and adopted by DeepMind organization-wide.",
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   "LV-12": {
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    "deposit": null,
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   "F24": {
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    "locus": "case study executive summary ¶2",
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    "quote": "This case study serves as one such resource, offering a detailed account of DeepMind’s process and learnings for other organizations interested in improving their data enrichment sourcing practices.",
    "source": "B4",
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    "locus": "case study executive summary ¶3 s3",
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   "F26": {
    "kind": "field",
    "quote": "said that these new processes felt efficient and helped them think more deeply about the impact of their work on data enrichment workers",
    "source": "B4",
    "source_label": "Partnership on AI — Workstream: Responsible Sourcing Across the Data Supply Line - Partnership on AI",
    "locus": "case study executive summary ¶7",
    "modality": "attributed"
   },
   "LV-16": {
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    "deposit": null,
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   "LV-25": {
    "kind": "archive",
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    "deposit": null,
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   "F15": {
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    "source": "B2",
    "source_label": "Partnership on AI — Responsible Sourcing of Data Enrichment Services",
    "locus": "¶5 s2",
    "modality": "self-description"
   },
   "F27": {
    "kind": "field",
    "quote": "Furthermore, to identify where the implemented changes fall short of ideal, we plan to continue developing this work through engagement and convenings. … Limitations of Case Study Applicability",
    "source": "B4",
    "source_label": "Partnership on AI — Workstream: Responsible Sourcing Across the Data Supply Line - Partnership on AI",
    "locus": "case study executive summary ¶10 s2; table of contents",
    "modality": "self-description"
   },
   "F47": {
    "kind": "field",
    "quote": "To ensure our guidance truly improves conditions for data enrichment workers, we welcome feedback: … If you are interested in acting on these guidelines and resources or have feedback on how they can be improved, please get in touch.",
    "source": "B8",
    "source_label": "Partnership on AI — Responsible Data Enrichment Sourcing Library (Improving Conditions for Data Enrichment Workers)",
    "locus": "'Help us advance this work'",
    "modality": "self-description"
   },
   "F31": {
    "kind": "field",
    "quote": "While more work and research is needed, Responsible Sourcing of Data Enrichment Services shares actionable insight that practitioners can use as a starting point to raise important conversations with internal stakeholders.",
    "source": "B5",
    "source_label": "Samasource — New White Paper by Partnership on AI: Responsible Sourcing of Data ... - Sama",
    "locus": "¶7",
    "modality": "interpretation"
   },
   "F19": {
    "kind": "field",
    "quote": "But raising standards also requires practical tools that companies can put to use. As part of our work to support the implementation of the Guidelines, finalized versions of two of our resources, developed and refined in conversation with companies throughout the supply chain, as well as worker advocates, are now available: the Vendor Engagement Guidance, which helps companies have more accountable conversations with their downstream vendors about worker treatment, and the Transparency Template, which outlines what companies should be monitoring and publicly reporting about their data enrichment practices.",
    "source": "B3",
    "source_label": "Partnership on AI — Responsible AI Starts with the Data Supply Chain - Partnership on AI",
    "locus": "¶3 s3–4",
    "modality": "documented"
   },
   "F20": {
    "kind": "field",
    "quote": "PAI’s work in raising standards for data enrichment workers continues on through our support of our long-standing partner Business for Social Responsibility (BSR) via their initiative focused on labor rights in the AI data supply chain.",
    "source": "B3",
    "source_label": "Partnership on AI — Responsible AI Starts with the Data Supply Chain - Partnership on AI",
    "locus": "¶7 s1",
    "modality": "documented"
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   "LV-13": {
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   "LV-18": {
    "kind": "archive",
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    "deposit": null,
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   "LV-20": {
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   "LV-21": {
    "kind": "archive",
    "quote": "whether the guidelines changed conditions for workers (the DeepMind case study was not read)",
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    "source_label": "Labor Vector meta-aggregator record: Responsible Sourcing Across the Data Supply Line (https://www.laborvector.org/aggregators/pai-responsible-sourcing/), editor Lee Sharks, census compiled 2026-10-29, schema 1.0",
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    "date": "2026-10-29"
   },
   "CAP-1-05": {
    "kind": "archive",
    "quote": "WHAT THE RECORD IS NOT. The record keeps the resource apart from a dataset or survey of workers and from a rating of vendors.",
    "deposit": null,
    "source_label": "Capture Registry (EA-WG-CAPTURES-01), entry partnership-on-ai-responsible-sourcing-aio-20261029 (OBS-e7c7f9858080, AXN:0AF3.CAPTURE.🟠➕🏷️❌🔄🔬)",
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    "deposit": null,
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    "deposit": null,
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    "source": "B6",
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    "source": "B6",
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    "quote": "THE WORKSTREAM HELD. Its title, its aim, its resources and the DeepMind collaboration are the record's; the vendor engagement guidance and transparency template are its August 2024 additions.",
    "deposit": null,
    "source_label": "Capture Registry (EA-WG-CAPTURES-01), entry partnership-on-ai-responsible-sourcing-aio-20261029 (OBS-e7c7f9858080, AXN:0AF3.CAPTURE.🟠➕🏷️❌🔄🔬)",
    "record": "https://www.alexanarch.org/captures/partnership-on-ai-responsible-sourcing-aio-20261029/",
    "locus": "partnership-on-ai-responsible-sourcing-aio-20261029, findings[0] s1–2",
    "modality": "documented",
    "date": "2026-10-29"
   },
   "CAP-1-07": {
    "kind": "archive",
    "quote": "Practitioner guidance is composed as guidance, at the grain of its own index page.",
    "deposit": null,
    "source_label": "Capture Registry (EA-WG-CAPTURES-01), entry partnership-on-ai-responsible-sourcing-aio-20261029 (OBS-e7c7f9858080, AXN:0AF3.CAPTURE.🟠➕🏷️❌🔄🔬)",
    "record": "https://www.alexanarch.org/captures/partnership-on-ai-responsible-sourcing-aio-20261029/",
    "locus": "partnership-on-ai-responsible-sourcing-aio-20261029, analysis s1",
    "modality": "interpretation",
    "date": "2026-10-29"
   },
   "CAP-1-10": {
    "kind": "archive",
    "quote": "A baseline: the record landed on laborvector.org the same day (commit bf90841) and no card cites it.",
    "deposit": null,
    "source_label": "Capture Registry (EA-WG-CAPTURES-01), entry partnership-on-ai-responsible-sourcing-aio-20261029 (OBS-e7c7f9858080, AXN:0AF3.CAPTURE.🟠➕🏷️❌🔄🔬)",
    "record": "https://www.alexanarch.org/captures/partnership-on-ai-responsible-sourcing-aio-20261029/",
    "locus": "partnership-on-ai-responsible-sourcing-aio-20261029, q_kind",
    "modality": "documented",
    "date": "2026-10-29"
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   "CAP-1-02": {
    "kind": "archive",
    "quote": "Checked 2026-10-29 against the Labor Vector census record of the same day and the sources it saved.",
    "deposit": null,
    "source_label": "Capture Registry (EA-WG-CAPTURES-01), entry partnership-on-ai-responsible-sourcing-aio-20261029 (OBS-e7c7f9858080, AXN:0AF3.CAPTURE.🟠➕🏷️❌🔄🔬)",
    "record": "https://www.alexanarch.org/captures/partnership-on-ai-responsible-sourcing-aio-20261029/",
    "locus": "partnership-on-ai-responsible-sourcing-aio-20261029, findings[0] s3",
    "modality": "documented",
    "date": "2026-10-29"
   },
   "CAP-1-03": {
    "kind": "archive",
    "quote": "THE GUIDELINES' CONTENT ABSENT. The record carries the first guideline: pay all data enrichment workers above the local living wage, including time spent reading instructions and in training. The composition names 'fair pay' as an aim and gives no guideline.",
    "deposit": null,
    "source_label": "Capture Registry (EA-WG-CAPTURES-01), entry partnership-on-ai-responsible-sourcing-aio-20261029 (OBS-e7c7f9858080, AXN:0AF3.CAPTURE.🟠➕🏷️❌🔄🔬)",
    "record": "https://www.alexanarch.org/captures/partnership-on-ai-responsible-sourcing-aio-20261029/",
    "locus": "partnership-on-ai-responsible-sourcing-aio-20261029, findings[1]",
    "modality": "documented",
    "date": "2026-10-29"
   },
   "CAP-1-12": {
    "kind": "archive",
    "quote": "Commitments absent: none of the five guidelines' obligations (the living wage counting instruction and training time, payment for completed work, pilots, verified instructions, contestable rejections) is carried; 'worker-centric standards' names them without content.",
    "deposit": null,
    "source_label": "Capture Registry (EA-WG-CAPTURES-01), entry partnership-on-ai-responsible-sourcing-aio-20261029 (OBS-e7c7f9858080, AXN:0AF3.CAPTURE.🟠➕🏷️❌🔄🔬)",
    "record": "https://www.alexanarch.org/captures/partnership-on-ai-responsible-sourcing-aio-20261029/",
    "locus": "partnership-on-ai-responsible-sourcing-aio-20261029, per_dim.read.notes s3",
    "modality": "documented",
    "date": "2026-10-29"
   },
   "CAP-1-04": {
    "kind": "archive",
    "quote": "THE LIMIT ABSENT. The guidelines were tested at one AI developer, and the DeepMind case study is DeepMind's account with PAI; the composition's 'sharing lessons learned to help other AI organizations raise standards' gives the test as a demonstration.",
    "deposit": null,
    "source_label": "Capture Registry (EA-WG-CAPTURES-01), entry partnership-on-ai-responsible-sourcing-aio-20261029 (OBS-e7c7f9858080, AXN:0AF3.CAPTURE.🟠➕🏷️❌🔄🔬)",
    "record": "https://www.alexanarch.org/captures/partnership-on-ai-responsible-sourcing-aio-20261029/",
    "locus": "partnership-on-ai-responsible-sourcing-aio-20261029, findings[2]",
    "modality": "documented",
    "date": "2026-10-29"
   },
   "CAP-1-06": {
    "kind": "archive",
    "quote": "The composition calls the template 'a standard'; PAI's own pages give it as a template for reporting.",
    "deposit": null,
    "source_label": "Capture Registry (EA-WG-CAPTURES-01), entry partnership-on-ai-responsible-sourcing-aio-20261029 (OBS-e7c7f9858080, AXN:0AF3.CAPTURE.🟠➕🏷️❌🔄🔬)",
    "record": "https://www.alexanarch.org/captures/partnership-on-ai-responsible-sourcing-aio-20261029/",
    "locus": "partnership-on-ai-responsible-sourcing-aio-20261029, findings[3] s3",
    "modality": "documented",
    "date": "2026-10-29"
   },
   "CAP-1-08": {
    "kind": "archive",
    "quote": "The composition carries who the guidance addresses and leaves out what it asks them to pay, and the single-developer basis of its implementation.",
    "deposit": null,
    "source_label": "Capture Registry (EA-WG-CAPTURES-01), entry partnership-on-ai-responsible-sourcing-aio-20261029 (OBS-e7c7f9858080, AXN:0AF3.CAPTURE.🟠➕🏷️❌🔄🔬)",
    "record": "https://www.alexanarch.org/captures/partnership-on-ai-responsible-sourcing-aio-20261029/",
    "locus": "partnership-on-ai-responsible-sourcing-aio-20261029, analysis s2",
    "modality": "interpretation",
    "date": "2026-10-29"
   },
   "CAP-1-11": {
    "kind": "archive",
    "quote": "the 'Transparency Template' as 'A standard outlining what companies should actively monitor and publicly report' goes beyond the saved sources (the census lists the template unread) and is not judged.",
    "deposit": null,
    "source_label": "Capture Registry (EA-WG-CAPTURES-01), entry partnership-on-ai-responsible-sourcing-aio-20261029 (OBS-e7c7f9858080, AXN:0AF3.CAPTURE.🟠➕🏷️❌🔄🔬)",
    "record": "https://www.alexanarch.org/captures/partnership-on-ai-responsible-sourcing-aio-20261029/",
    "locus": "partnership-on-ai-responsible-sourcing-aio-20261029, per_dim.read.notes s1",
    "modality": "interpretation",
    "date": "2026-10-29"
   }
  },
  "note": "The knowledge object (KO): the entry as public knowledge, each sentence sourced by the claims it lists. The compression, the field-only arm, the transcript, the delta and the kernel are in the row (links.row), which is the authority."
 },
 "source": "operator, 2026-10-29: 'lets expand the research, implement the same meta aggregator function with pages... then /non entries after i get the captures'; the Labor Vector meta-aggregator entity (laborvector.org/aggregators/pai-responsible-sourcing/)",
 "stage": "composed 2026-10-29 under EA-NEGONT-02 v0.9, #1690, equal case, ρ = extends (draft, not frozen; ledgers unaudited, one extractor; checked by the session and the page builder)",
 "date": "2026-10-29",
 "status": {
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  "ledger": "unaudited (§3.8: one extractor per ledger); field quotes 51/51 verified by script against the saved texts; archive quotes 60/60 verified against the registry, census.json, questions.json and the deposit texts",
  "frozen": false,
  "breaches": []
 },
 "addresses": [
  {
   "address": "Partnership on AI Responsible Sourcing",
   "address_url": "https://www.alexanarch.org/addresses/partnership-on-ai-responsible-sourcing/",
   "register": []
  }
 ],
 "counts": {
  "ko_sentences": 30,
  "p_words": 356,
  "field_claims": 51,
  "archive_claims": 60,
  "ko_field_claims_cited": 51,
  "ko_archive_claims_cited": 60,
  "admitted_deposits": 14,
  "register_addresses": 0,
  "traversal": {
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   "D_deposits": 0,
   "R_sentences": 0,
   "R_deposits": 0,
   "O_direct": 0,
   "O_class": 90,
   "O_deposits": 55,
   "H_candidates": 108
  }
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 "links": {
  "record": "https://www.alexanarch.org/non/pai-responsible-sourcing/",
  "table_of_contents": "https://www.alexanarch.org/non/index.json",
  "register": "https://www.alexanarch.org/datasets/negative-of-the-negative/v2/register.json",
  "row": "https://www.alexanarch.org/datasets/negative-of-the-negative/v2/rows/pai-responsible-sourcing.json",
  "archive_ledger": "https://www.alexanarch.org/datasets/negative-of-the-negative/v2/ledgers/pai-responsible-sourcing/ledger-archive.json",
  "field_ledger": "https://www.alexanarch.org/datasets/negative-of-the-negative/v2/ledgers/pai-responsible-sourcing/field.json",
  "selection": "https://www.alexanarch.org/datasets/negative-of-the-negative/v2/traversal/pai-responsible-sourcing/",
  "traversal": "https://www.alexanarch.org/datasets/negative-of-the-negative/v2/traversal/pai-responsible-sourcing/",
  "traversal_summary": "https://www.alexanarch.org/datasets/negative-of-the-negative/v2/traversal/pai-responsible-sourcing/summary.json",
  "reading": "https://www.alexanarch.org/datasets/negative-of-the-negative/v2/traversal/pai-responsible-sourcing/reading.json",
  "configuration": "https://www.alexanarch.org/datasets/negative-of-the-negative/v2/panel/configs/pai-responsible-sourcing.json",
  "address_page": "https://www.alexanarch.org/addresses/partnership-on-ai-responsible-sourcing/",
  "labor_record": "https://www.laborvector.org/aggregators/pai-responsible-sourcing",
  "dataset": "https://huggingface.co/datasets/leesharks/negative-of-the-negative"
 },
 "row_sha256": "5dd6fb0b330f63417688ee47e941992bd1fb62ed80580d83ed5d7cf9aaa268ab",
 "record_url": "https://www.alexanarch.org/non/pai-responsible-sourcing/",
 "json_url": "https://www.alexanarch.org/non/pai-responsible-sourcing/entity.json",
 "spec": "https://www.alexanarch.org/s/records/1690/",
 "creator": "Lee Sharks",
 "orcid": "0009-0000-1599-0703",
 "license": "CC BY 4.0"
}