{"id":28786,"date":"2025-09-01T13:48:15","date_gmt":"2025-09-01T11:48:15","guid":{"rendered":"https:\/\/opentalk.iit.it\/?p=28786"},"modified":"2025-09-01T13:56:40","modified_gmt":"2025-09-01T11:56:40","slug":"making-ai-more-accessible-and-reusable-results-from-the-skills4eosc-project","status":"publish","type":"post","link":"https:\/\/opentalk.iit.it\/en\/making-ai-more-accessible-and-reusable-results-from-the-skills4eosc-project\/","title":{"rendered":"Making AI More Accessible and Reusable: Results from the Skills4EOSC Project"},"content":{"rendered":"

Artificial Intelligence (AI) <\/span><\/b>and Machine Learning (ML)<\/b> are becoming increasingly central to scientific research. However, their adoption presents significant challenges regarding transparency<\/b>, documentation<\/b>, and reusability<\/b><\/h4>\n

<\/p>\n

While the FAIR principles (Findable, Accessible, Interoperable, Reusable)<\/strong><\/a> are now widely applied to research data, and more recently to scientific software, their application to AI models is still in its early stages and lacks established guidelines.<\/p>\n

The Italian Institute of Technology (IIT)<\/strong>, as an affiliated partner of the European project Skills4EOSC<\/strong><\/a>coordinated by GARR<\/strong>, recently completed a Delphi study<\/strong><\/a> aimed at building expert consensus on a set of FAIR practices specifically intended for ML\/AI models.<\/p>\n

A collaborative and structured study<\/strong><\/h4>\n

The Delphi study, a structured method designed to achieve consensus through iterative expert consultation, was carried out in collaboration with TU Delft<\/strong><\/a> and KIT<\/strong><\/a>. More than 200 AI and ML experts<\/strong> from universities, research centers, and global networks were contacted. Of these, 67 <\/strong>actively participated in the study, including experts from our institute.<\/p>\n

The study was conducted in collaboration with researchers from participating\u00a0 institutions and the FAIR4ML Interest Group<\/strong><\/a> of the Research Data Alliance (RDA)<\/strong><\/a>. Members of this group contributed to defining and refining the list of practices and participated throughout the process.<\/p>\n

The study included two rounds of online questionnaires and concluded with an online meeting in January 2025<\/strong>, where results and future perspectives were discussed with some of the participants.<\/p>\n

The Outcome: 10 FAIR best practices for AI and ML models<\/strong><\/h4>\n

The final outcome is a shared list of 10 best practices<\/strong> essential to make AI and ML models more findable<\/strong>, accessible<\/strong>, and reusable<\/strong>, in line with the FAIR principles. These practices cover the entire model lifecycle, from documentation to sharing and reuse.<\/p>\n

The results were presented in a Lightning Talk<\/strong><\/a> at the 19th International Digital Curation Conference (IDCC25)<\/strong><\/a> on February 18, 2025<\/strong>. All details of the study are available in the official deliverable published on Zenodo<\/strong><\/a> and summarized in a Booklet<\/strong><\/a> designed for researchers.<\/p>\n

A Contribution to Research Data Management and Open Science<\/strong><\/h4>\n

\u201cThis contribution fully aligns with the mission of Skills4EOSC, which aims to build a training ecosystem for Open Science in Europe, and it reinforces IIT\u2019s role as a promoter of innovation and best practices in scientific data management.\u201d<\/em>
\n\u2014 Valentina Pasquale, Data Steward Coordinator at IIT<\/p>\n

This work represents a concrete step toward the adoption of FAIR principles in technologically advanced domains such as AI and ML, contributing to more open<\/strong>, collaborative<\/strong>, and responsible<\/strong> research.<\/p>\n

A distinctive feature of this initiative was the strong involvement of both external and internal scientific communities<\/strong> at IIT, which actively contributed from the early stages of the study.<\/p>\n

\u201cBeing involved in this study from the very beginning allowed me to connect with international experts and collaboratively identify best practices to make AI and ML models more FAIR. It was also a valuable opportunity to engage IIT researchers and explore how to bring the project\u2019s outcomes into the Institute. The next phase will involve collaborating with researchers to turn these findings into practical guidelines for implementing FAIR practices in our context.\u201d<\/em>
\n\u2014 Elda Osmenaj, Data Steward at IIT<\/p>\n

\n

Image credit:<\/strong> \u00a9 Khanchit Khirisutchalual<\/a> \/iStock, Image ID: 1515913422.<\/p>\n","protected":false},"excerpt":{"rendered":"

Artificial Intelligence (AI) and Machine Learning (ML) are becoming increasingly central to scientific research. However, their adoption presents significant challenges regarding transparency, documentation, and reusability<\/p>\n","protected":false},"author":30,"featured_media":28784,"comment_status":"closed","ping_status":"closed","sticky":true,"template":"","format":"standard","meta":{"footnotes":""},"categories":[5038,4552,5039],"tags":[8237],"class_list":["post-28786","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-iniziative-en","category-en-news","category-news-en","tag-openscience_iit-en"],"yoast_head":"\nMaking AI More Accessible and Reusable: Results from the Skills4EOSC Project - Talk iit<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/opentalk.iit.it\/en\/making-ai-more-accessible-and-reusable-results-from-the-skills4eosc-project\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Making AI More Accessible and Reusable: Results from the Skills4EOSC Project - Talk iit\" \/>\n<meta property=\"og:description\" content=\"Artificial Intelligence (AI) and Machine Learning (ML) are becoming increasingly central to scientific research. 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