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Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claims

Authors :
Brundage, Miles
Avin, Shahar
Wang, Jasmine
Belfield, Haydn
Krueger, Gretchen
Hadfield, Gillian
Khlaaf, Heidy
Yang, Jingying
Toner, Helen
Fong, Ruth
Maharaj, Tegan
Koh, Pang Wei
Hooker, Sara
Leung, Jade
Trask, Andrew
Bluemke, Emma
Lebensold, Jonathan
O'Keefe, Cullen
Koren, Mark
Ryffel, Théo
Rubinovitz, JB
Besiroglu, Tamay
Carugati, Federica
Clark, Jack
Eckersley, Peter
Haas, Sarah de
Johnson, Maritza
Laurie, Ben
Ingerman, Alex
Krawczuk, Igor
Askell, Amanda
Cammarota, Rosario
Lohn, Andrew
Krueger, David
Stix, Charlotte
Henderson, Peter
Graham, Logan
Prunkl, Carina
Martin, Bianca
Seger, Elizabeth
Zilberman, Noa
hÉigeartaigh, Seán Ó
Kroeger, Frens
Sastry, Girish
Kagan, Rebecca
Weller, Adrian
Tse, Brian
Barnes, Elizabeth
Dafoe, Allan
Scharre, Paul
Herbert-Voss, Ariel
Rasser, Martijn
Sodhani, Shagun
Flynn, Carrick
Gilbert, Thomas Krendl
Dyer, Lisa
Khan, Saif
Bengio, Yoshua
Anderljung, Markus
Brundage, Miles
Avin, Shahar
Wang, Jasmine
Belfield, Haydn
Krueger, Gretchen
Hadfield, Gillian
Khlaaf, Heidy
Yang, Jingying
Toner, Helen
Fong, Ruth
Maharaj, Tegan
Koh, Pang Wei
Hooker, Sara
Leung, Jade
Trask, Andrew
Bluemke, Emma
Lebensold, Jonathan
O'Keefe, Cullen
Koren, Mark
Ryffel, Théo
Rubinovitz, JB
Besiroglu, Tamay
Carugati, Federica
Clark, Jack
Eckersley, Peter
Haas, Sarah de
Johnson, Maritza
Laurie, Ben
Ingerman, Alex
Krawczuk, Igor
Askell, Amanda
Cammarota, Rosario
Lohn, Andrew
Krueger, David
Stix, Charlotte
Henderson, Peter
Graham, Logan
Prunkl, Carina
Martin, Bianca
Seger, Elizabeth
Zilberman, Noa
hÉigeartaigh, Seán Ó
Kroeger, Frens
Sastry, Girish
Kagan, Rebecca
Weller, Adrian
Tse, Brian
Barnes, Elizabeth
Dafoe, Allan
Scharre, Paul
Herbert-Voss, Ariel
Rasser, Martijn
Sodhani, Shagun
Flynn, Carrick
Gilbert, Thomas Krendl
Dyer, Lisa
Khan, Saif
Bengio, Yoshua
Anderljung, Markus
Source :
arXiv vol.2020 (2020) date: 2020-04-20 [ISSN 2331-8422]
Publication Year :
2020

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.

Details

Database :
OAIster
Journal :
arXiv vol.2020 (2020) date: 2020-04-20 [ISSN 2331-8422]
Notes :
Brundage, Miles
Publication Type :
Electronic Resource
Accession number :
edsoai.on1359189214
Document Type :
Electronic Resource