Gendered algorithms

Do hiring algorithms discriminate against CVs with feminine language? This interdisciplinary project investigates how and to what extent predictive hiring systems can discriminate against women and CVs with feminine language.

Summary

In the past decade, the adoption of predictive systems in recruitment continues to rise. Central to this are the presumptions that predictive hiring systems enable not only high efficiency but also impartiality: unbiased calculations that lead to mathematical predictions. However, if predictive hiring systems make decisions based on historical data, and data reflects society and its historical biases and assumptions, then these systems are not immune to bias. On the contrary, they have been shown to perpetuate, and sometimes exacerbate societal biases.

Our project builds upon findings from socio-psychological research that corporate recruitment often discriminates against women. With the rising adoption of automation in corporate decisions, we complement extant research by shedding light on how gendered language may exacerbate biased recruitment decisions.

Aim

This project aims to understand whether and how gender reflects in CVs of applicants, which will subsequently enable to study the sensitivity of predictive recruitment algorithms to those signals. The objectives are as follows.

1. To identify the feminine and masculine language in CVs written by applicants of different genders, expanding on studies such as Gaucher, Friesen & Kay, 2011 and De-Arteaga et al., 2019.

2. To identify the prevalence of gender-indicative language in CVs in specific industries with certain gender dominance

Methodologies

1. We obtain a minimum of 2,000 CVs using a crowdsourcing platform such as Prolific.

2. We use Natural Language Processing and classification techniques to analyse the CVs and identify language specific to male and female authors, respectively. We assess the levels of gendered language against the industries towards which the CVs are targeted.

Researchers

Chief Investigator
  • A/Prof Leah Ruppanner, Associate Professor
    School of Social and Political Sciences
    Faculty of Arts
    • Co-Investigators
    • Dr Marc Cheong, Research Fellow in Digital Ethics
      School of Computing and Information Systems
      Faculty of Engineering and Information Technology
    • Dr Lea Frermann, Lecturer
      School of Computing and Information Systems
      Faculty of Engineering and Information Technology
    • Sheilla Njoto, Research Assistant, Graduate Researcher
      The Policy Lab, School of Social and Political Sciences
      Faculty of Arts