Corpus ID: 215768885

Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claims

@article{Brundage2020TowardTA,
  title={Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claims},
  author={M. Brundage and S. Avin and J. Wang and Haydn Belfield and G. Kr{\"u}ger and Gillian K. Hadfield and Heidy Khlaaf and Jingying Yang and Helen Toner and Ruth Fong and Tegan Maharaj and Pang Wei Koh and Sara Hooker and J. Leung and A. Trask and Emma Bluemke and Jonathan Lebensbold and Cullen O'Keefe and Mark Koren and T. Ryffel and J. Rubinovitz and T. Besiroglu and Federica Carugati and J. Clark and P. Eckersley and Sarah de Haas and M. Johnson and B. Laurie and Alex Ingerman and Igor Krawczuk and Amanda Askell and R. Cammarota and A. Lohn and D. Krueger and C. Stix and P. Henderson and L. Graham and C. Prunkl and Bianca Martin and E. Seger and N. Zilberman and Se'an 'O h'Eigeartaigh and Frens Kroeger and Girish Sastry and R. Kagan and Adrian Weller and B. Tse and E. Barnes and A. Dafoe and Paul Scharre and Ariel Herbert-Voss and Martijn Rasser and Shagun Sodhani and C. Flynn and T. Gilbert and Lisa Dyer and S. Khan and Yoshua Bengio and Markus Anderljung},
  journal={ArXiv},
  year={2020},
  volume={abs/2004.07213}
}
  • M. Brundage, S. Avin, +56 authors Markus Anderljung
  • Published 2020
  • Business, Computer Science
  • ArXiv
  • 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… CONTINUE READING
    16 Citations
    Trustworthy AI Inference Systems: An Industry Research View.
    Rapid Trust Calibration through Interpretable and Uncertainty-Aware AI
    • 4
    Mediating Artificial Intelligence Developments through Negative and Positive Incentives
    A Review of the ICO’s Draft Guidance on the AI Auditing Framework
    • 1
    • PDF
    Assurance levels for decision making in autonomous intelligent systems and their safety
    On Controllability of AI
    Comprehensible Counterfactual Interpretation on Kolmogorov-Smirnov Test

    References

    SHOWING 1-10 OF 249 REFERENCES
    Increasing Trust in AI Services through Supplier's Declarations of Conformity
    • 69
    • PDF
    Closing the AI accountability gap: defining an end-to-end framework for internal algorithmic auditing
    • 19
    • PDF
    Algorithmic Accountability in the Administrative State
    • 3
    • PDF
    The global landscape of AI ethics guidelines
    • 152
    • PDF
    The Windfall Clause: Distributing the Benefits of AI for the Common Good
    • 1
    • PDF
    Linking Artificial Intelligence Principles
    • 17
    • PDF
    Accountable Algorithms
    • 127
    • PDF
    The Role of Cooperation in Responsible AI Development
    • 9
    • PDF