There is a blind spot in AI research

3 min read Original article ↗
  • Comment
  • Published:

Nature volume 538pages 311–313 (2016)Cite this article

Subjects

Fears about the future impacts of artificial intelligence are distracting researchers from the real risks of deployed systems, argue Kate Crawford and Ryan Calo.

This is a preview of subscription content, access via your institution

Relevant articles

Open Access articles citing this article.

Access options

Subscribe to this journal

Receive 51 print issues and online access

$199.00 per year

only $3.90 per issue

Buy this article

  • Purchase on SpringerLink
  • Instant access to the full article PDF.

USD 39.95

Prices may be subject to local taxes which are calculated during checkout

References

  1. Domingos, P. The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World (Allen Lane, 2015).

  2. Barocas, S. & Selbst, A. D. Calif. Law Rev. 104, 671–732 (2016).

    Google Scholar 

  3. Sweeney, L. Discrimination in Online Ad Delivery (2013); available at http://dx.doi.org/10.2139/ssrn.2208240

  4. Armstrong, S. & Orseau, L. in Uncertainty in Artificial Intelligence: Proceedings of the Thirty-Second Conference (eds Ihler, A. & Janzing, D.) 557–566 (AUAI Press, 2016); available at http://go.nature.com/2drokil

  5. Friedman, B., Kahn, P. H. & Borning, A. in Human–Computer Interaction in Management Information Systems: Foundation (eds Zhang, P. & Galletta, D.) 348–372 (M. E. Sharpe, 2006); available at http://go.nature.com/2dee8om

  6. Bostrom, N. Superintelligence: Paths, Dangers, Strategies (Oxford Univ. Press, 2016).

  7. Lin, P. in Autonomes Fahren: Technische, Rechtliche und Gesellschaftliche Aspekte (eds Maurer, M., Gerdes, J. C., Lenz, B. & Winner, H.) 69–85 (Springer, 2015); available at http://doi.org/brdw

  8. Saunders, J., Hunt, P. & Hollywood, J. S. J. Exp. Criminol. 12, 347–371 (2016).

    Article  Google Scholar 

  9. Caruana, R. et al. ‘Intelligible models for healthcare: predicting pneumonia risk and hospital 30-day readmission’ Proc. 21th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining 1721–1730 (ACM, 2015).

  10. Crawford, K. et al. The AI Now Report: The Social and Economic Implications of Artificial Intelligence Technologies in the Near-Term (2016); available at http://artificialintelligencenow.com

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Kate Crawford is a principal researcher at Microsoft Research in New York City, a visiting professor at the Massachusetts Institute of Technology in Cambridge, Massachusetts, and a senior research fellow at New York University, New York, USA.,

    Kate Crawford

  2. Ryan Calo is an assistant professor of law and of information science (by courtesy), and faculty co-director of the Tech Policy Lab at the University of Washington, Seattle, Washington, USA.,

    Ryan Calo

Authors

  1. Kate Crawford
  2. Ryan Calo

Corresponding authors

Correspondence to Kate Crawford or Ryan Calo.

Additional information

Related links

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Crawford, K., Calo, R. There is a blind spot in AI research. Nature 538, 311–313 (2016). https://doi.org/10.1038/538311a

Download citation

  • Published:

  • Issue date:

  • DOI: https://doi.org/10.1038/538311a

This article is cited by