Toward Trustworthy AI: Mechanisms for Supporting Verifiable Claims

Report by Miles Brundage, Shahar Avin, Jasmine Wang, Haydn Belfield, Gretchen Krueger, Gillian Hadfield, Heidy Khlaaf, Jingying Yang, Helen Toner, Ruth Fong, Tegan Maharaj, Pang Wei Koh, Sara Hooker, Jade Leung, Andrew Trask, Emma Bluemke, Jon Lebensold, Cullen O'Keefe, Mark Koren, Théo Ryffel, JB Rubinovitz, Tamay Besiroglu, Federica Carugati, Jack Clark, Peter Eckersley, Sarah de Haas, Martiza Johnson, Ben Laurie, Alex Ingerman, Igor Krawczuk, Amanda Askell, Rosario Cammarota, Andrew Lohn, Shagun Sodhani, Charlotte Stix, Peter Henderson, Logan Graham, Carina Prunkl, Bianca Martin, Elizabeth Seger, Noa Zilberman, Seán Ó hÉigeartaigh, Frens Kroeger, Girish Sastry, Rebecca Kagan, Adrian Weller, Brian Tse, Beth Barnes, Allan Dafoe, Paul Scharre, Martijn Rasser, David Kreuger, Carrick Flynn, Ariel Herbert-Voss, Thomas Krendl Gilbert, Lisa Dyer, Saif Khan, Markus Anderljung, Yoshua Bengio
Published on 16 April 2020


The increasingly widespread application of AI research has brought growing awareness of the risks posed by 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 users, civil society, governments, and other stakeholders, there is a need to move beyond principles to a focus on mechanisms for demonstrating responsible behavior. Making and assessing verifiable claims, to which developers can be held accountable, is one step in this direction.

This report suggests various steps that different stakeholders can take to make it easier to verify claims made about AI systems and their associated development processes. The authors believe the implementation of such mechanisms can help make progress on one component of the multifaceted problem of ensuring that AI development is conducted in a trustworthy fashion.

Read full report

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