Portrait of Jasmine Wang is unavailable

Jasmine Wang

Alumni

Publications

Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claims
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
Jonathan Lebensold
Cullen O'Keefe
Mark Koren
Théo Ryffel … (see 39 more)
JB Rubinovitz
Tamay Besiroglu
Federica Carugati
Jack Clark
Peter Eckersley
Sarah de Haas
Maritza Johnson
Ben Laurie
Alex Ingerman
Igor Krawczuk
Amanda Askell
Rosario Cammarota
Andrew Lohn
David Krueger
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
Elizabeth Barnes
Allan Dafoe
Paul Scharre
Ariel Herbert-Voss
Martijn Rasser
Shagun Sodhani
Carrick Flynn
Thomas Krendl Gilbert
Lisa Dyer
Saif Khan
Yoshua Bengio
Markus Anderljung
With the recent wave of progress in artificial intelligence (AI) has come a growing awareness of the large-scale impacts of AI systems, and … (see more)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.
2019-12-31
arXiv (preprint)
doi.org
arxiv.org