Towards Openness Beyond Open Access: User Journeys through 3 Open AI Collaboratives

• URL: http://arxiv.org/abs/2301.08488v1
• Date: Fri, 20 Jan 2023 09:34:59 GMT
• Title: Towards Openness Beyond Open Access: User Journeys through 3 Open AI Collaboratives
• Authors: Jennifer Ding, Christopher Akiki, Yacine Jernite, Anne Lee Steele, Temi Popo
• Abstract summary: We focus on three such communities, each focused on a different kind of activity around AI. First, we document the community structures that facilitate these distributed, volunteer-led teams. Through interviews with community leaders, we map user journeys for how members discover, join, contribute, and participate.
• Score: 3.9324706525398017
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Open Artificial Intelligence (Open source AI) collaboratives offer alternative pathways for how AI can be developed beyond well-resourced technology companies and who can be a part of the process. To understand how and why they work and what additionality they bring to the landscape, we focus on three such communities, each focused on a different kind of activity around AI: building models (BigScience workshop), tools and ways of working (The Turing Way), and ecosystems (Mozilla Festival's Building Trustworthy AI Working Group). First, we document the community structures that facilitate these distributed, volunteer-led teams, comparing the collaboration styles that drive each group towards their specific goals. Through interviews with community leaders, we map user journeys for how members discover, join, contribute, and participate. Ultimately, this paper aims to highlight the diversity of AI work and workers that have come forth through these collaborations and how they offer a broader practice of openness to the AI space.

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