Without open data there is no open AI (and no open science generally). "However," the authors write (9 page PDF), "the challenge of producing and openly disseminating data that are easily discoverable, accessible, interoperable, and reusable (FAIR) has emerged as a significant concern for policymakers." This article attempts to explore the reasons for that. To make a long story short, even if the researcher intends to abide by FAIR principles, institutions and repositories often have policies and regulations that make compliance difficult. "While some institutional repositories enforce policies like restricted open access, which reduces the visibility and reusability of research outputs, others impose eligibility restrictions on deposits, raise copyright concerns, and require funding for maintenance." I've argued with people that the default should be 'open', while restrictions should be the exception that requires justification. But that's the opposite of most institutions today.
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