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.

Computational audits combat disparities in recognition

Nature Human Behaviour volume 6, pages 473–474 (2022)Cite this article

Subjects

To the editor—Recognition by peers and the general public can greatly affect the trajectory of a scientist’s career. Any recognition — the inclusion of one’s name in print as an expert, an invited lecture or an award — paves the way to future accolades. Regardless of an individual’s merit, biases can skew which scientists are recognized. This is exemplified in a recent study of disparities in scientific journalism, which found that individuals with names that are predicted to be of East Asian origin were underquoted and undermentioned1. Regular monitoring of recognition practices can combat this by identifying overlooked groups and creating opportunities to hold the recognizing body accountable.

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

Access options

Rent or buy this article

Prices vary by article type

from$1.95

to$39.95

Learn more

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

References

  1. Davidson, N. R. & Greene, C. S. Analysis of scientific journalism in Nature reveals gender and regional disparities in coverage. Preprint at https://doi.org/10.1101/2021.06.21.449261 (2021).

  2. LaFrance, A. I analyzed a year of my reporting for gender bias (again). The Atlantic, https://go.nature.com/3E3UVOm (17 February 2016)

  3. Yong, E. I spent two years trying to fix the gender imbalance in my stories. The Atlantic https://go.nature.com/3q4ah0j (6 February 2018).

  4. Le, T. T., Himmelstein, D. S., Hippen, A. A., Gazzara, M. R. & Greene, C. S. Cell Systems 12, 900–906 (2021).

    Article  CAS  PubMed  Google Scholar 

  5. Martin, J. L. PLoS Comput. Biol. 10, e1003903 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  6. Matias, J. N. How to ethically and responsibly identify gender in large datasets. mediashift.org, https://go.nature.com/3E2p00r (21 November 2014).

  7. Imai, K. & Khanna, K. Polit. Anal. 24, 263–272 (2016).

    Article  Google Scholar 

  8. Demografix. Determine the gender of a name. genderize.io, https://genderize.io/ (accessed 3 November 2021).

  9. Malmasi, S. & Dras, M. A data-driven approach to studying given names and their gender and ethnicity associations. In Proc. Australasian Language Technology Association Workshop (eds Ferraro, G. & Wan, S.) 145–149 (2014).

  10. Bertolero, M. A. et al. Racial and ethnic imbalance in neuroscience reference lists and intersections with gender. Preprint at https://doi.org/10.1101/2020.10.12.336230 (2020).

Download references

Author information

Author notes

  1. These authors contributed equally: Ariel A. Hippen, Natalie R. Davidson.

Authors and Affiliations

  1. Department of Systems Pharmacology and Translational Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Ariel A. Hippen

  2. Center for Health Artificial Intelligence, University of Colorado Anschutz Medical Campus, Aurora, CO, USA

    Natalie R. Davidson & Casey S. Greene

Authors
  1. Ariel A. Hippen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Natalie R. Davidson
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Casey S. Greene
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Casey S. Greene.

Ethics declarations

Competing interests

The authors declare no competing interests.

Rights and permissions

Reprints and Permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Hippen, A.A., Davidson, N.R. & Greene, C.S. Computational audits combat disparities in recognition. Nat Hum Behav 6, 473–474 (2022). https://doi.org/10.1038/s41562-021-01279-2

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41562-021-01279-2

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