Article(electronic)#1April 27, 2021
The changing forms of corruption in China
In: Crime, law and social change: an interdisciplinary journal, Volume 75, Issue 3, p. 247-265
ISSN: 1573-0751
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Article(electronic)#32022
SEC Regulation and Non-GAAP Income Statements
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Open Access#92020
Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claims
In: Brundage , M , Avin , S , Wang , J , Belfield , H , Krueger , G , Hadfield , G , Khlaaf , H , Yang , J , Toner , H , Fong , R , Maharaj , T , Koh , P W , Hooker , S , Leung , J , Trask , A , Bluemke , E , Lebensold , J , O'Keefe , C , Koren , M , Ryffel , T , Rubinovitz , JB , Besiroglu , T , Carugati , F , Clark , J , Eckersley , P , Haas , S D , Johnson , M , Laurie , B , Ingerman , A , Krawczuk , I , Askell , A , Cammarota , R , Lohn , A , Krueger , D , Stix , C , Henderson , P , Graham , L , Prunkl , C , Martin , B , Seger , E , Zilberman , N , hÉigeartaigh , S Ó , Kroeger , F , Sastry , G , Kagan , R , Weller , A , Tse , B , Barnes , E , Dafoe , A , Scharre , P , Herbert-Voss , A , Rasser , M , Sodhani , S , Flynn , C , Gilbert , T K , Dyer , L , Khan , S , Bengio , Y & Anderljung , M 2020 , ' Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claims ' , arXiv.org, e-Print Archive, Mathematics .
Brundage, Miles; Avin, Shahar; Wang, Jasmine; Belfield, Haydn; Krueger, Gretchen; Hadfield, Gillian; Khlaaf, Heidy; Yang, Jingying
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
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.
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