While you’re here… help us stay here.

Are you enjoying open access to policy and research published by a broad range of organisations? Please donate today so that we can continue to provide this service.


Help wanted: an examination of hiring algorithms, equity, and bias

Employment Discrimination Digital disruption Labour market disruption Algorithms United States of America
Attachment Size
apo-nid210071.pdf 5.31 MB

The hiring process is a critical gateway to economic opportunity, determining who can access consistent work to support themselves and their families. Employers have long used digital technology to manage their hiring decisions, and now many are turning to new predictive hiring tools to inform each step of their hiring process.

This report explores how predictive tools affect equity throughout the entire hiring process. We explore popular tools that many employers currently use, and offer recommendations for further scrutiny and reflection. We conclude that without active measures to mitigate them, bias will arise in predictive hiring tools by default.

Key reflections:

  • Hiring is rarely a single decision point, but rather a cumulative series of small decisions. Predictive technologies can play very different roles throughout the hiring funnel, from determining who sees job advertisements, to estimating an applicant's performance, to forecasting a candidate's salary requirements.
  • While new hiring tools rarely make affirmative hiring decisions, they often automate rejections. Much of this activity happens early in the hiring process, when job opportunities are automatically surfaced to some people and withheld from others, or when candidates are deemed by a predictive system not to meet the minimum or desired qualifications needed to move further in the application process.
  • Predictive hiring tools can reflect institutional and systemic biases, and removing sensitive characteristics is not a solution. Predictions based on past hiring decisions and evaluations can both reveal and reproduce patterns of inequity at all stages of the hiring process, even when tools explicitly ignore race, gender, age, and other protected attributes.
  • Nevertheless, vendors' claim that technology can reduce interpersonal bias should not be ignored. Bias against people of color, women, and other underrepresented groups has long plagued hiring, but with more deliberation, transparency, and oversight, some new hiring technologies might be poised to help improve on this poor baseline.
  • Even before people apply for jobs, predictive technology plays a powerful role in determining who learns of open positions. Employers and vendors are using sourcing tools, like digital advertising and personalized job boards, to proactively shape their applicant pools. These technologies are outpacing regulatory guidance, and are exceedingly difficult to study from the outside.
  • Hiring tools that assess, score, and rank jobseekers can overstate marginal or unimportant distinctions between similarly qualified candidates. In particular, rankordered lists and numerical scores may influence recruiters more than we realize, and not enough is known about how human recruiters act on predictive tools' guidance.
Publication Details
License type:
Access Rights Type: