Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claims

Abstract

With the recent wave of progress in artificial intelligence (AI) has come a growing awareness of the large-scale impacts of AI systems, and recognition that existing regulations and norms in industry and academia are insufficient to ensure responsible AI development. In order for AI developers to earn trust from system users, customers, civil society, governments, and other stakeholders that they are building AI responsibly, they will need to make verifiable claims to which they can be held accountable. Those outside of a given organization also need effective means of scrutinizing such claims. This report suggests various steps that different stakeholders can take to improve the verifiability of claims made about AI systems and their associated development processes, with a focus on providing evidence about the safety, security, fairness, and privacy protection of AI systems. We analyze ten mechanisms for this purpose--spanning institutions, software, and hardware--and make recommendations aimed at implementing, exploring, or improving those mechanisms.

Cited by in corpus (10)

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• Harms from Increasingly Agentic Algorithmic Systems
• Ethics-Based Auditing of Automated Decision-Making Systems: Intervention Points and Policy Implications
• Filling gaps in trustworthy development of AI
• Towards Training Reproducible Deep Learning Models
• Certification Labels for Trustworthy AI: Insights From an Empirical Mixed-Method Study
• CLEVR-X: A Visual Reasoning Dataset for Natural Language Explanations
• Foundations for the Future: Institution building for the purpose of Artificial Intelligence governance
• Advancing Trustworthy AI for Sustainable Development: Recommendations for Standardising AI Incident Reporting