Responsible AI in Open Ecosystems: Reconciling Innovation with Risk Assessment and Disclosure

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# Description

The rapid scaling of AI has intensified attention on ethical development and deployment, prompting new auditing standards, transparency requirements, and governance frameworks designed to limit harms to people and communities.

At this critical juncture we review the practical challenges of promoting responsible AI and transparency in informal, open ecosystems that power essential infrastructure. We focus on how evaluation practices both enable and constrain honest examinations of model limitations, biases, and downstream risks.

Our controlled analysis of 7,903 Hugging Face projects shows that risk documentation is strongly associated with evaluation habits, yet submissions from the platform's most prominent competitive leaderboard displayed less accountability among top performers. These insights can guide AI providers, policymakers, and legal scholars in crafting interventions that preserve open innovation while rewarding ethical adoption.