Filling gaps in trustworthy development of AI

@article{Avin2021FillingGI,
  title={Filling gaps in trustworthy development of AI},
  author={Shahar Avin and Haydn Belfield and Miles Brundage and Gretchen Krueger and Jasmine Wang and Adrian Weller and Markus Anderljung and Igor Krawczuk and David M. Krueger and Jonathan Lebensold and Tegan Maharaj and Noa Zilberman},
  journal={Science},
  year={2021},
  volume={374 6573},
  pages={
          1327-1329
        }
}
[Figure: see text]. 
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7 Citations
Background Citations
2
Methods Citations
1

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References

SHOWING 1-8 OF 8 REFERENCES

Trustworthy artificial intelligence

A data-driven research framework for TAI is developed and its utility is demonstrated by delineating fruitful avenues for future research, particularly with regard to the distributed ledger technology-based realization of TAI.

The global landscape of AI ethics guidelines

A detailed analysis of 84 AI ethics reports around the world finds a convergence around core principles but substantial divergence on practical implementation, highlighting the importance of integrating guideline-development efforts with substantive ethical analysis and adequate implementation strategies.

Calibrating Noise to Sensitivity in Private Data Analysis

The study is extended to general functions f, proving that privacy can be preserved by calibrating the standard deviation of the noise according to the sensitivity of the function f, which is the amount that any single argument to f can change its output.

Governing AI safety through independent audits

This Perspective proposes a pragmatic approach where independent audit of AI systems is central and would embody three AAA governance principles: prospective risk Assessments, operation Audit trails and system Adherence to jurisdictional requirements.

ISO / TC 22 Road Vehicles " Report on standardisation prospective for automated vehicles ( RoSPAV ) " ( 2021

  • Belfield . Yale J . L . & Tech

Lins , A . Sunyaev

  • Electron Markets

Report on standardisation prospective for automated vehicles (RoSPAV)

  • ISO/TC 22 Road Vehicles
  • 2021

The First Taxonomy of AI Incidents

  • Nat Mach Intell
  • 2021