{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,17]],"date-time":"2025-10-17T02:40:34Z","timestamp":1760668834926,"version":"build-2065373602"},"reference-count":119,"publisher":"Association for Computing Machinery (ACM)","issue":"7","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["Proc. ACM Hum.-Comput. Interact."],"published-print":{"date-parts":[[2025,10,18]]},"abstract":"<jats:p>\n            This paper provides guidance for building and maintaining infrastructure for participatory AI efforts by sharing reflections on building World Wide Dishes (WWD), a bottom-up, community-led image and text dataset of culinary dishes and associated cultural customs. We present WWD as an example of participatory dataset creation, where community members both guide the design of the research process and contribute to the crowdsourced dataset. This approach incorporates localised expertise and knowledge to address the limitations of web-scraped Internet datasets acknowledged in the Participatory AI discourse. We show that our approach can result in curated, high-quality data that supports decentralised contributions from communities that do not typically contribute to datasets due to a variety of systemic factors. Our project demonstrates the importance of participatory mediators in supporting community engagement by identifying the kinds of labour they performed to make WWD possible. We surface three dimensions of labour performed by\n            <jats:italic toggle=\"yes\">participatory mediators<\/jats:italic>\n            that are crucial for participatory dataset construction: building trust with community members, making participation accessible, and contextualising community values to support meaningful data collection. Drawing on our findings, we put forth five lessons for building infrastructure to support future participatory AI efforts.\n          <\/jats:p>","DOI":"10.1145\/3757673","type":"journal-article","created":{"date-parts":[[2025,10,16]],"date-time":"2025-10-16T17:06:01Z","timestamp":1760634361000},"page":"1-43","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["The Human Labour of Data Work: Capturing Cultural Diversity through\n            <scp>World Wide Dishes<\/scp>"],"prefix":"10.1145","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1520-4220","authenticated-orcid":false,"given":"Siobhan Mackenzie","family":"Hall","sequence":"first","affiliation":[{"name":"University of Oxford, Oxford, United Kingdom and Oxford Artificial Intelligence Society, Oxford, United Kingdom"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5604-055X","authenticated-orcid":false,"given":"Samantha","family":"Dalal","sequence":"additional","affiliation":[{"name":"Department of Information Science, University of Colorado Boulder, Boulder, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8650-671X","authenticated-orcid":false,"given":"Raesetje","family":"Sefala","sequence":"additional","affiliation":[{"name":"School of Computer Science, McGill University, Montreal, Canada and Distributed Artificial Intelligence Research Institute, Montreal, Canada"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2092-1013","authenticated-orcid":false,"given":"Foutse","family":"Yuehgoh","sequence":"additional","affiliation":[{"name":"KmerAI, Toulon, France"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-0953-1769","authenticated-orcid":false,"given":"Aisha","family":"Alaagib","sequence":"additional","affiliation":[{"name":"Independent researcher, Riyadh, Saudi Arabia"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-7413-5545","authenticated-orcid":false,"given":"Imane","family":"Hamzaoui","sequence":"additional","affiliation":[{"name":"\u00c9cole nationale Sup\u00e9rieure d'Informatique Algiers, Algiers, Algeria and New York University Abu Dhabi, Abu Dhabi, United Arab Emirates"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1391-4886","authenticated-orcid":false,"given":"Shu","family":"Ishida","sequence":"additional","affiliation":[{"name":"Autodesk, London, United Kingdom and Oxford Artificial Intelligence Society, Oxford, United Kingdom"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-1088-6122","authenticated-orcid":false,"given":"Jabez","family":"Magomere","sequence":"additional","affiliation":[{"name":"University of Oxford, Oxford, United Kingdom and Oxford Artificial Intelligence Society, Oxford, United Kingdom"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5003-3538","authenticated-orcid":false,"given":"Lauren","family":"Crais","sequence":"additional","affiliation":[{"name":"Faculty of Law, University of Oxford, Oxford, United Kingdom and Oxford Artificial Intelligence Society, Oxford, United Kingdom"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7111-7677","authenticated-orcid":false,"given":"Aya","family":"Salama","sequence":"additional","affiliation":[{"name":"Independent Researcher, New Cairo, Egypt"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0639-9668","authenticated-orcid":false,"given":"Tejumade","family":"Afonja","sequence":"additional","affiliation":[{"name":"CISPA Helmholtz Center for Information Security, Saarbr\u00fccken, Germany and AI Saturdays Lagos, Lagos, Nigeria"}]}],"member":"320","published-online":{"date-parts":[[2025,10,16]]},"reference":[{"key":"e_1_2_1_1_1","unstructured":"[n.d.]. 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