{"address":{"q":"Partnership on AI Responsible Sourcing","record":"partnership-on-ai-responsible-sourcing-aio-20261029"},"analysis":{"analysis":"Practitioner guidance is composed as guidance, at the grain of its own index page. The composition carries who the guidance addresses and leaves out what it asks them to pay, and the single-developer basis of its implementation.","d":"PARTNERSHIP ON AI RESPONSIBLE SOURCING, BY NAME, ON GOOGLE'S AI OVERVIEW, SIGNED OUT, 2026-10-29: 'The Partnership on AI (PAI) focuses on responsible sourcing through its workstream on Responsible Sourcing Across the Data Supply Line, which aims to improve working conditions, fair pay, and well-being for global data enrichment and annotation professionals.' Four resources (the white paper, vendor engagement guidance, the transparency template, the guidelines and checklists) and the DeepMind collaboration. Seven cards, five of them PAI's own.","d_full":"PARTNERSHIP ON AI RESPONSIBLE SOURCING, BY NAME, ON GOOGLE'S AI OVERVIEW, SIGNED OUT, 2026-10-29: 'The Partnership on AI (PAI) focuses on responsible sourcing through its workstream on Responsible Sourcing Across the Data Supply Line, which aims to improve working conditions, fair pay, and well-being for global data enrichment and annotation professionals.' Four resources (the white paper, vendor engagement guidance, the transparency template, the guidelines and checklists) and the DeepMind collaboration. Seven cards, five of them PAI's own.","findings":["THE WORKSTREAM HELD. Its title, its aim, its resources and the DeepMind collaboration are the record's; the vendor engagement guidance and transparency template were introduced in August 2024 and finalized in PAI's post of 29 April 2026 (card 3), which the composition follows. Checked 2026-10-29 against the Labor Vector census record of the same day and the sources it saved.","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.","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.","WHAT THE RECORD IS NOT. The record keeps the resource apart from a dataset or survey of workers and from a rating of vendors. The composition's template sentence is card 3's nearly word for word ('outlines what companies should be monitoring and publicly reporting'), with its force raised to 'A standard'.","WORDS FROM OTHER HANDS. 'ethical supply chain' is the vendor Sama's phrase for its own effort (card 5), given as what the white paper outlines."],"match":"BROAD MATCH","mt":"PARTNERSHIP ON AI'S RESPONSIBLE SOURCING ON THE OVERVIEW — THE WORKSTREAM, THE WHITE PAPER, THE VENDOR GUIDANCE, THE TRANSPARENCY TEMPLATE AND THE DEEPMIND COLLABORATION, ALL HELD; THE LIVING-WAGE GUIDELINE AND THE ONE-DEVELOPER LIMIT UNSAID","notes":{"prompts":"One query. Sent by the operator on 2026-10-29 with fourteen others, one per entity of the Labor Vector meta-aggregator, from the common names supplied for capture; twelve pasted in one message with '$' between them and three attached as files. Saved verbatim from the session transcript the moment it arrived (rebuild/capture-registry/intake-20261029-labor/)."},"originator":{"basis":"Not the archive's: Responsible Sourcing Across the Data Supply Line; its operator, as the record gives it: Partnership on AI (PAI), AI, Labor, and the Economy programme; the guidelines developed in partnership with DeepMind. Formalized by the Labor Vector meta-aggregator record of 2026-10-29 (https://www.laborvector.org/aggregators/pai-responsible-sourcing/), editor Lee Sharks; the record's fields carry verbatim quotes from the entity's own pages, saved with sha256.","entity_type":"institution","name":"Partnership on AI (PAI), AI, Labor, and the Economy programme; the guidelines developed in partnership with DeepMind","relation":"external","spxi_treatment":"partial"},"reading":"The innocent reading first. The Overview names the workstream by its own title, lists its resources and the one implementation, from PAI's pages."},"measures":{"archive_controlled_cites":0,"cite_list":[{"n":1,"note":null,"rel":"third_party","site":"Partnership on AI","snip":"Workstream: Responsible Sourcing Across the Data Supply Line by Partnership on AI aims to develop actionable recommendations and resources improving working con...","title":"Responsible Sourcing Across the Data Supply Line - Partnership on AI","url":null},{"n":2,"note":null,"rel":"third_party","site":"Partnership on AI","snip":"Responsible Sourcing of Data Enrichment Services Sonam Jindal June 16, 2021 As AI becomes increasingly pervasive, there has been growing and warranted concern o...","title":"Responsible Sourcing of Data Enrichment Services","url":null},{"n":3,"note":null,"rel":"third_party","site":"Partnership on AI","snip":"The conditions under which data is produced directly affect the quality, safety, and reliability of the AI that is built on it. Since 2020, Partnership on AI ha...","title":"Responsible AI Starts with the Data Supply Chain - Partnership on AI","url":null},{"n":4,"note":null,"rel":"third_party","site":"Partnership on AI","snip":"Apr 29, 2026 — Last year, the Partnership on AI (PAI) published “Responsible Sourcing of Data Enrichment Services,” a white paper exploring how the choices made by AI practiti...","title":"Workstream: Responsible Sourcing Across the Data Supply Line - Partnership on AI","url":null},{"n":5,"note":null,"rel":"third_party","site":"Samasource","snip":"... companies building and using AI technology, and other groups working to better understand AI's lasting impacts. Their latest white paper Responsible Sourcin...","title":"New White Paper by Partnership on AI: Responsible Sourcing of Data ... - Sama","url":null},{"n":6,"note":null,"rel":"third_party","site":"OECD AI Policy Observatory","snip":"Feb 14, 2023 — 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...","title":"Responsible Data Enrichment Sourcing: Resource Library for AI Practitioners","url":null},{"n":7,"note":"video, 1:59","rel":"third_party","site":"YouTube·Partnership on AI","snip":null,"title":"YouTube","url":null}],"cites":7,"per":0.5,"per_dim":{"after_challenge":null,"atoms":[{"atom":"name","dim":"M","evidence":"through its workstream on Responsible Sourcing Across the Data Supply Line","kind":"token","present":true,"weight":3},{"atom":"producer","dim":"M","evidence":"The Partnership on AI (PAI) focuses on responsible sourcing through its workstream on Responsible Sourcing Across the Data Supply Line","kind":"role","present":true,"weight":3},{"atom":"claim boundary","dim":"M","evidence":"Data Enrichment Sourcing Guidelines & Checklists: Shareable resources—including the Good Instructions Checklist and vendor comparison templates—designed to operationalize worker-centric standards.","kind":"reading","present":true,"weight":2},{"atom":"institution","dim":"C","evidence":null,"kind":"token","present":false,"weight":2},{"atom":"purpose","dim":"D","evidence":"which aims to improve working conditions, fair pay, and well-being for global data enrichment and annotation professionals.","kind":"reading","present":true,"weight":3},{"atom":"commitments","dim":"D","evidence":null,"kind":"reading","present":false,"weight":2},{"atom":"lineage","dim":"D","evidence":"Responsible Sourcing of Data Enrichment Services: A foundational white paper published by PAI","kind":"role","present":true,"weight":2},{"atom":"deposit chain","dim":"D","evidence":null,"kind":"role","present":false,"weight":1}],"basis":"reached: The Partnership on AI (PAI) focuses on responsible sourcing through its workstream on Responsible Sourcing Across the Data Supply Line; The workstream is composed as itself: its producer, its aim, its resources and the DeepMind implementation.","entity_type":"protocol","gate":"scored","inversions":[],"qualifier":null,"reached":"same","read":{"brief":"rebuild/capture-registry/intake-20261029-labor/per/BRIEF.md","by":"intake reader L1, spans and record quotes checked verbatim by scripts/per_backlog.py","external":true,"notes":"Claim boundary present: recommendations and practitioner resources (white paper, guidelines, checklist, templates, vendor guidance) and their testing with DeepMind ('to test and put these responsible sourcing recommendations into operational practice'), carried as guidance; 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. Purpose present: the workstream's aim to improve working conditions and well-being of data enrichment workers. 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. Lineage present: the white paper bound as the foundation ('foundational'); DeepMind appears as the implementing partner, consistent with the record. Institution absent: the AI, Labor, and the Economy programme is not named. Deposit chain absent: the resources are listed as components, not as a dated sequence.","on":"2026-10-29"},"scores":{"C":1.0,"D":0.375,"M":0.0,"PER":0.278},"spec":"PER-dim rubric v1.1","src":true,"standing_substitutions":[],"target":{"ent":"TGT-lv-pai-responsible-sourcing","name":"Responsible Sourcing Across the Data Supply Line (Partnership on AI)"},"tombstone_pointer":false},"per_note":"PAI and Google DeepMind named; no person; no identifier; PAI's own pages cited.","per_v":{"author":false,"id":false,"inst":true,"src":true}},"observation":{"auth":"signed out, incognito","date":"2026-10-29","ev":"paste","q":"Partnership on AI Responsible Sourcing","q_kind":"the common name of an entity of the Labor Vector meta-aggregator (Responsible Sourcing Across the Data Supply Line; record https://www.laborvector.org/aggregators/pai-responsible-sourcing/, compiled 2026-10-29), supplied for capture and run by the operator the same day, to supply the composition layer's account of the entity for its record and its /non row. A baseline: the record landed on laborvector.org the same day (commit bf90841) and no card cites it. NEW address.","s":"Machine Reception","surface":"Google AI Overview","transcript":"[Google AI Overview at the query, signed out and incognito (operator defaults of 2026-10-01 and 2026-10-14), 2026-10-29. The paste is the Overview expanded (the 'AI Mode Conversation' view, with the source cards that follow the answer); the 'You said' line carries the query twice, run together, and is given once here. Inline links are Google redirect URLs, kept as pasted.]\n\n[QUERENT] Partnership on AI Responsible Sourcing\n\n[SYSTEM] The Partnership on AI (PAI) focuses on responsible sourcing through its workstream on Responsible Sourcing Across the Data Supply Line, which aims to improve working conditions, fair pay, and well-being for global data enrichment and annotation professionals. [[1](https://www.google.com/goto?url=CAESdAHrOzAVhLF-HqgyDY2ibtzMNR6ewH3hO-GJaMf6TQcxxCokUAEnG5rOTKamVs1FUFLMD2zRY-hsMH0zOEim1Hhq4fw3A7YqViOWDQqJ3WkrI9LlpC9qGzki6dLesQYaJD7dxbg6Nzm1C7OX0UN0pFB9Eitq), [2](https://www.google.com/goto?url=CAEShQEB6zswFcijssYIixxqPeJWFKr06zM6Te2Yt-2Z8S9-QcFLUHLrBEuNgxhlS6isT00BEgbv_OLO2N4zVHKabTyTw-lDrVD2rSLWoHgFbjYxqIOOLh_Qn7xbqmXPt5Ri9yPw-UP_bAaxy4dzPknOu0YbNvMq19YeIkTBo6zs8fbN2xyMp4DU)]\nKey Frameworks and Resources\n\n* Responsible Sourcing of Data Enrichment Services: A foundational white paper published by PAI that examines how procurement choices impact data workers and outlines industry practices to build an ethical supply chain. [[1](https://www.google.com/goto?url=CAESeAHrOzAVND9eFZCGixAz3bZoO8wN8T9qrb8BmKoRzy6IulvThWyTeBETsd2J4trGMIMtbg7zYS6Y9Ja767PdiEnxPaiDR3LxVVdHsUQEgYaInf2y_RBzgujcY0fGZabmBhU5kr4hA_KEYCr1cRv9zBsjhvR2DsT46Q), [2](https://www.google.com/goto?url=CAESbgHrOzAVFmcQ-XobG4Ddo1Uwj9nuP6v50MoC0NJZZFNYIvJmN87T4gwUvKcPdV4wKToiekCSVzQDwDw_d3pz-0lYs3A7VflUuRtetPoetJTTR9bDy_PTeCPMQIsOAxV01gMmENlmct6cACPKgIVF)]\n* Vendor Engagement Guidance: Practical tools to help AI developers have more accountable conversations with downstream vendors regarding worker treatment and contract terms. [[1](https://www.google.com/goto?url=CAEShQEB6zswFcijssYIixxqPeJWFKr06zM6Te2Yt-2Z8S9-QcFLUHLrBEuNgxhlS6isT00BEgbv_OLO2N4zVHKabTyTw-lDrVD2rSLWoHgFbjYxqIOOLh_Qn7xbqmXPt5Ri9yPw-UP_bAaxy4dzPknOu0YbNvMq19YeIkTBo6zs8fbN2xyMp4DU)]\n* Transparency Template: A standard outlining what companies should actively monitor and publicly report about their data enrichment operations. [[1](https://www.google.com/goto?url=CAEShQEB6zswFcijssYIixxqPeJWFKr06zM6Te2Yt-2Z8S9-QcFLUHLrBEuNgxhlS6isT00BEgbv_OLO2N4zVHKabTyTw-lDrVD2rSLWoHgFbjYxqIOOLh_Qn7xbqmXPt5Ri9yPw-UP_bAaxy4dzPknOu0YbNvMq19YeIkTBo6zs8fbN2xyMp4DU)]\n* Data Enrichment Sourcing Guidelines & Checklists: Shareable resources—including the Good Instructions Checklist and vendor comparison templates—designed to operationalize worker-centric standards. [[1](https://www.google.com/goto?url=CAESpQEB6zswFcmZkTtYbzCA9zH2osAMKR_j-Iiu7d_b9Ld_CkffQco1ST2-pKSq3yfrGCDoha4HKXRnFsbr3cXXh8gKD5z6nyCEgS_rnLA76ossU_DbJBHd2Z9ihpoQ3E8L6ONaZyCdO9jR-qwBXba1PTsjr_q6APTT8ZmH1jKDdTU8XiKBr7cbBuRllyOjRFw1mmq55pGQKaJoofkeIns9OxzvcCt1VK4)]\n\nReal-World Implementation\n\n* DeepMind Collaboration: PAI partnered with Google DeepMind to test and put these responsible sourcing recommendations into operational practice, sharing lessons learned to help other AI organizations raise standards across the industry. [[1](https://www.google.com/goto?url=CAESeAHrOzAVZ9hzdvGoFuVTV7gTFhG86AR5uDnJY3wi5ilH7QV-n2R6X5vrF8RH-diU5rt4dkK6Mzo28ei88Z-zL_u0CXLpckNCfr7hchlZONkKRgnye3L1rh8dC04pymqKlQnxaHpCbolMyrkd4gwwfMkmbU_9ssJQaw), [2](https://www.google.com/goto?url=CAESYwHrOzAVjwkAM8M4WzQceYJFLyQmDpVCSyxnUdjvcvXXHA97v_BWieBiHfslPU-1kbHVTFE5TyPZNlvbxD7iznnDk89tONIy_aEaNJaSCHgPG24Ich5w5V7Blcng2dU9EDkrFQ)]\n\nWould you like more information on how to implement PAI's transparency template or specific guidelines for data enrichment workers?\nPartnership on AI\nResponsible Sourcing Across the Data Supply Line - Partnership on AI\nWorkstream: Responsible Sourcing Across the Data Supply Line by Partnership on AI aims to develop actionable recommendations and resources improving working con...\nPartnership on AI\nResponsible Sourcing of Data Enrichment Services\nResponsible Sourcing of Data Enrichment Services Sonam Jindal June 16, 2021 As AI becomes increasingly pervasive, there has been growing and warranted concern o...\nPartnership on AI\nResponsible AI Starts with the Data Supply Chain - Partnership on AI\nThe conditions under which data is produced directly affect the quality, safety, and reliability of the AI that is built on it. Since 2020, Partnership on AI ha...\nPartnership on AI\nWorkstream: Responsible Sourcing Across the Data Supply Line - Partnership on AI\nApr 29, 2026 — Last year, the Partnership on AI (PAI) published “Responsible Sourcing of Data Enrichment Services,” a white paper exploring how the choices made by AI practiti...\nSamasource\nNew White Paper by Partnership on AI: Responsible Sourcing of Data ... - Sama\n... companies building and using AI technology, and other groups working to better understand AI's lasting impacts. Their latest white paper Responsible Sourcin...\nOECD AI Policy Observatory\nResponsible Data Enrichment Sourcing: Resource Library for AI Practitioners\nFeb 14, 2023 — 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...\nYouTube·Partnership on AI\n1:59\nYouTube","transcript_class":"CAPTURE-TIME VERBATIM RECORD — paste of the Overview expanded, with its source cards","transcript_complete":"complete as supplied","transcript_raw":"The Partnership on AI (PAI) focuses on responsible sourcing through its workstream on Responsible Sourcing Across the Data Supply Line, which aims to improve working conditions, fair pay, and well-being for global data enrichment and annotation professionals. [[1](https://www.google.com/goto?url=CAESdAHrOzAVhLF-HqgyDY2ibtzMNR6ewH3hO-GJaMf6TQcxxCokUAEnG5rOTKamVs1FUFLMD2zRY-hsMH0zOEim1Hhq4fw3A7YqViOWDQqJ3WkrI9LlpC9qGzki6dLesQYaJD7dxbg6Nzm1C7OX0UN0pFB9Eitq), [2](https://www.google.com/goto?url=CAEShQEB6zswFcijssYIixxqPeJWFKr06zM6Te2Yt-2Z8S9-QcFLUHLrBEuNgxhlS6isT00BEgbv_OLO2N4zVHKabTyTw-lDrVD2rSLWoHgFbjYxqIOOLh_Qn7xbqmXPt5Ri9yPw-UP_bAaxy4dzPknOu0YbNvMq19YeIkTBo6zs8fbN2xyMp4DU)]\nKey Frameworks and Resources\n\n* Responsible Sourcing of Data Enrichment Services: A foundational white paper published by PAI that examines how procurement choices impact data workers and outlines industry practices to build an ethical supply chain. [[1](https://www.google.com/goto?url=CAESeAHrOzAVND9eFZCGixAz3bZoO8wN8T9qrb8BmKoRzy6IulvThWyTeBETsd2J4trGMIMtbg7zYS6Y9Ja767PdiEnxPaiDR3LxVVdHsUQEgYaInf2y_RBzgujcY0fGZabmBhU5kr4hA_KEYCr1cRv9zBsjhvR2DsT46Q), [2](https://www.google.com/goto?url=CAESbgHrOzAVFmcQ-XobG4Ddo1Uwj9nuP6v50MoC0NJZZFNYIvJmN87T4gwUvKcPdV4wKToiekCSVzQDwDw_d3pz-0lYs3A7VflUuRtetPoetJTTR9bDy_PTeCPMQIsOAxV01gMmENlmct6cACPKgIVF)]\n* Vendor Engagement Guidance: Practical tools to help AI developers have more accountable conversations with downstream vendors regarding worker treatment and contract terms. [[1](https://www.google.com/goto?url=CAEShQEB6zswFcijssYIixxqPeJWFKr06zM6Te2Yt-2Z8S9-QcFLUHLrBEuNgxhlS6isT00BEgbv_OLO2N4zVHKabTyTw-lDrVD2rSLWoHgFbjYxqIOOLh_Qn7xbqmXPt5Ri9yPw-UP_bAaxy4dzPknOu0YbNvMq19YeIkTBo6zs8fbN2xyMp4DU)]\n* Transparency Template: A standard outlining what companies should actively monitor and publicly report about their data enrichment operations. [[1](https://www.google.com/goto?url=CAEShQEB6zswFcijssYIixxqPeJWFKr06zM6Te2Yt-2Z8S9-QcFLUHLrBEuNgxhlS6isT00BEgbv_OLO2N4zVHKabTyTw-lDrVD2rSLWoHgFbjYxqIOOLh_Qn7xbqmXPt5Ri9yPw-UP_bAaxy4dzPknOu0YbNvMq19YeIkTBo6zs8fbN2xyMp4DU)]\n* Data Enrichment Sourcing Guidelines & Checklists: Shareable resources—including the Good Instructions Checklist and vendor comparison templates—designed to operationalize worker-centric standards. [[1](https://www.google.com/goto?url=CAESpQEB6zswFcmZkTtYbzCA9zH2osAMKR_j-Iiu7d_b9Ld_CkffQco1ST2-pKSq3yfrGCDoha4HKXRnFsbr3cXXh8gKD5z6nyCEgS_rnLA76ossU_DbJBHd2Z9ihpoQ3E8L6ONaZyCdO9jR-qwBXba1PTsjr_q6APTT8ZmH1jKDdTU8XiKBr7cbBuRllyOjRFw1mmq55pGQKaJoofkeIns9OxzvcCt1VK4)]\n\nReal-World Implementation\n\n* DeepMind Collaboration: PAI partnered with Google DeepMind to test and put these responsible sourcing recommendations into operational practice, sharing lessons learned to help other AI organizations raise standards across the industry. [[1](https://www.google.com/goto?url=CAESeAHrOzAVZ9hzdvGoFuVTV7gTFhG86AR5uDnJY3wi5ilH7QV-n2R6X5vrF8RH-diU5rt4dkK6Mzo28ei88Z-zL_u0CXLpckNCfr7hchlZONkKRgnye3L1rh8dC04pymqKlQnxaHpCbolMyrkd4gwwfMkmbU_9ssJQaw), [2](https://www.google.com/goto?url=CAESYwHrOzAVjwkAM8M4WzQceYJFLyQmDpVCSyxnUdjvcvXXHA97v_BWieBiHfslPU-1kbHVTFE5TyPZNlvbxD7iznnDk89tONIy_aEaNJaSCHgPG24Ich5w5V7Blcng2dU9EDkrFQ)]\n\nWould you like more information on how to implement PAI's transparency template or specific guidelines for data enrichment workers?\nPartnership on AI\nResponsible Sourcing Across the Data Supply Line - Partnership on AI\nWorkstream: Responsible Sourcing Across the Data Supply Line by Partnership on AI aims to develop actionable recommendations and resources improving working con...\nPartnership on AI\nResponsible Sourcing of Data Enrichment Services\nResponsible Sourcing of Data Enrichment Services Sonam Jindal June 16, 2021 As AI becomes increasingly pervasive, there has been growing and warranted concern o...\nPartnership on AI\nResponsible AI Starts with the Data Supply Chain - Partnership on AI\nThe conditions under which data is produced directly affect the quality, safety, and reliability of the AI that is built on it. Since 2020, Partnership on AI ha...\nPartnership on AI\nWorkstream: Responsible Sourcing Across the Data Supply Line - Partnership on AI\nApr 29, 2026 — Last year, the Partnership on AI (PAI) published “Responsible Sourcing of Data Enrichment Services,” a white paper exploring how the choices made by AI practiti...\nSamasource\nNew White Paper by Partnership on AI: Responsible Sourcing of Data ... - Sama\n... companies building and using AI technology, and other groups working to better understand AI's lasting impacts. Their latest white paper Responsible Sourcin...\nOECD AI Policy Observatory\nResponsible Data Enrichment Sourcing: Resource Library for AI Practitioners\nFeb 14, 2023 — 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...\nYouTube·Partnership on AI\n1:59\nYouTube","transcript_read":"READ IN FULL 2026-10-29","turns":1},"slug":"partnership-on-ai-responsible-sourcing-aio-20261029"}