Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

MACHINE LEARNING

Gazing into Clever Hans machines

Nature Machine Intelligence volume 1, pages 172–173 (2019)Cite this article

Subjects

Humans infer much of the intentions of others by just looking at their gaze. Similarly, we want to understand how machine learning systems solve a problem. New tools are developed to find out what strategies a learning machine is using, such as what it is paying attention to when classifying images.

Clever Hans (pictured) was the infamous early twentieth century horse that solved arithmetic problems by simply following the cues that inadvertently emanated from his trainer’s body language. The horse performed well, but only as long as the choreography of the show was kept the same. Measuring performance tells us how well a system works for specific conditions. However, it does not explain what strategies are being used to solve a particular problem and often fails to extrapolate when the context changes2.

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

Relevant articles

Open Access articles citing this article.

Access options

Rent or buy this article

Get just this article for as long as you need it

$39.95

Learn more

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

References

  1. Lapuschkin, S. et al. Nat. Commun. https://doi.org/10.1038/s41467-019-08987-4 (2019).

  2. Sturm, B. J. IEEE Trans. Multimedia 16, 1636–1644 (2014).

    Article  Google Scholar 

  3. Barrouillet, P., Mignon, M. & Thevenot, C. J. Exp. Child Psychol. 99, 233–251 (2008).

    Article  Google Scholar 

  4. Rajalingham, R. et al. J. Neurosci. 38, 7255–7269 (2018).

    Article  Google Scholar 

  5. Greydanus, S., Koul, A., Dodge, J. & Fern, A. Preprint at https://arxiv.org/abs/1711.00138 (2017).

  6. Bach, S. et al. PLOS ONE 10, e0130140 (2015).

    Article  Google Scholar 

  7. Ribeiro, M. T., Singh, S. & Guestrin, C. in Proc. 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 1135–1144 (ACM, 2016).

  8. Ribeiro, M. T., Singh, S. & Guestrin, C. in Thirty-Second AAAI Conference on Artificial Intelligence 1527–1535 (AAAI, 2018).

Download references

Author information

Authors and Affiliations

  1. Universitat Politecnica de Valencia, Sistemes Informatics i Computacio, Valencia, Spain

    José Hernández-Orallo

  2. Leverhulme Centre for the Future of Intelligence, University of Cambridge, Cambridge, UK

    José Hernández-Orallo

Authors
  1. José Hernández-Orallo
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to José Hernández-Orallo.

Ethics declarations

Competing interests

The author declares no competing interests.

Rights and permissions

Reprints and Permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Hernández-Orallo, J. Gazing into Clever Hans machines. Nat Mach Intell 1, 172–173 (2019). https://doi.org/10.1038/s42256-019-0032-5

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s42256-019-0032-5

This article is cited by

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing