COVID-19 has disrupted the lives of millions worldwide. It is estimated that unemployment in the US alone will hit 32% — that’s 47 million recent graduates, experienced professionals, minorities, and overseas professionals.1 There is no doubt companies will start to hire skilled and unskilled labour in substantial numbers as we pull through this crisis. But, the ‘new-normal’ presents new challenges for Human Resource (HR) managers. Ethnicity and race-based biases have become more entrenched — all while marginalized communities are suffering disproportionately from the impacts of the virus itself.
As we rebuild from COVID-19, individual and group identities will emerge based on one’s experience during the crisis. It might then become far too easy for us to judge others based on their past behavior; for example, compliance with quarantine rules, actual infection status, race,2 and perhaps most importantly, job status during the crisis. Such information could create pitfalls that HR managers may find themselves in when evaluating candidates.
Behavioral Science, Democratized
We make 35,000 decisions each day, often in environments that aren’t conducive to making sound choices.
At TDL, we work with organizations in the public and private sectors—from new startups, to governments, to established players like the Gates Foundation—to debias decision-making and create better outcomes for everyone.
Research shows that person-organization fit and person-job fit are established predictors of performance.3 However, these fits don’t always occur because of large informational asymmetries between organizations and job aspirants.4 For example, job applicants may be influenced by a range of factors including perceived job value, perceptions of interview performance, cultural norms, beliefs and interests, and even the wording of the job posting itself. Conversely, the recruiting team may unconsciously attribute certain qualities to specific demographics, and may have an affinity for people with characteristics similar to those who they are familiar with. From this, applicants may be discriminated against on account of their race, gender, or other demographic factors, including where they live or go to school.5,6
The AI Governance Challenge
In order to reduce the impact of these biases, HR managers can take the following steps:
Carefully craft job descriptions to remove bias
The wording of job ads matters. When job ads include more masculine than feminine wording, women find these jobs less appealing.7 For example, job postings that state “we will challenge our employees to be proud of their chosen career” or “you will develop leadership skills and learn business principles” are more likely to attract males when compared to “we nurture and support our employees, expecting that they will become committed to their career” or “you will develop interpersonal skills and understanding of business.” Ad wording can also impact how different demographic groups view the organization.8
Anonymize resumes to remove bias against specific groups of people
A racial gap in labour market outcomes exists – we know that African-Americans face differential treatment when searching for jobs such as getting fewer callbacks for each resume they send out.9,10 There is evidence to show that East Asians may face discrimination in the coming months as well.11 Research shows that bias can be removed by anonymizing resumes in the job screening process.
Evaluate candidates jointly to help reduce bias against the marginalized
Gender bias in the evaluation of job candidates exists across business, government, and academia. An “evaluation nudge”, in which candidates are evaluated jointly rather than separately, can stop evaluators from relying on cognitive shortcuts, such as group stereotypes. This will focus evaluators’ attention on what they should be doing — evaluating the ability of candidates. Joint evaluation can help address bias against groups other than women, as evaluators have access to more information than they would if they evaluated candidates separately.12
Use structured interviews and tests to ensure objectivity and fairness
The unstructured interview as a predictive technique is unreliable because of its lack of validity. Research suggests that structured interviews — in which the questions to be asked are predetermined and are directly related to the job — are far more effective in ensuring objectivity and fairness. HR managers must try and articulate attributes they look for in candidates as objectively measurable criteria.13
HR managers will have their hands full as they start evaluating millions of applicants who wish to re-enter the job market when the economy starts to recover. The financial and time-related costs of unbiasedly evaluating candidates are low, and the benefits can be long-lasting and immense. HR managers just need the will to do so.
- Davidson, Paul. (2020). Unemployment could top 32% as 47M workers are laid off amid coronavirus: St. Louis Fed. USA Today, retrieved from https://www.usatoday.com/
- Harvard Kennedy School. (2020). Big, If True Webinar: Race, Xenophobia, and COVID-19. Youtube, retrieved from https://www.youtube.com/
- Goodman, S. A, Svyantek, D. J. (1999). Person-Organization Fit and Contextual Performance: Do Shared Values Matter. Journal of Vocational Behavior, 254-275.
- Verquer, M. L., Beehr, T. A., Wagner, S. H. (2003). A meta-analysis of relations between person-organization fit and work attitudes. Journal of Vocational Behavior, 473-489.
- Greenwals, G. G., Banaji, M. R. (1995). Implicit Social Cognition: Attitudes, Self-Esteem, and Stereotypes. Psychological Review, Vol 102, No. 1, 4-27.
- Dawes, R. (1988). Rational Choice in an Uncertain World. USA: Harcourt Brace Jovanovich
- Gaucher, D., Friesen, J., Kay, A. C. (2011). Evidence That Gendered Wording in Job Advertisements Exists and Sustains Gender Inequality. Journal of Personality and Social Psychology, Vol. 101, No. 1, 109–128.
- Baguesa, M., Perez-Villadonigab, M. J. (2012). Do recruiters prefer applicants with similar skills? Evidence from a randomized natural experiment. Journal of Economic Behavior & Organization, 82, 12– 20.
- Bertrand, M., Mullainathan, S. (2004). Are Emily and Greg More Employable Than Lakisha and Jamal? A Field Experiment on Labor Market Discrimination. The American Economic Review, Vol. 94, No. 4, 991-1013.
- Ziegert, J. C., Hanges, P. J. (2005). Employment Discrimination: The Role of Implicit Attitudes, Motivation, and a Climate for Racial Bias. Journal of Applied Psychology, Vol. 90, No. 3, 553–562.
- Blanding, D., Solomon, D (2020). The Coronavirus Pandemic Is Fueling Fear and Hate Across America. Center for American Progress. Center for American Progress, retrieved at https://www.americanprogress.org
- Bohnet, I., van Geen, A., Bazerman, M. (2016). When Performance Trumps Gender Bias: Joint vs. Separate Evaluation. Management Science, 62(5), 1225-1234.
- Goldin, C., Rouse., C. (2000). Orchestrating Impartiality: The Impact of ‘Blind’ Auditions on Female Musicians. The American Economic Review, Vol. 90, No. 4, 715–741.
About the Author
Siddharth’s diverse education and experience feed his interest in the applicability of behavioral science in understanding our world and solving big problems. His work encompasses international development, consulting, finance, and social innovation. Apart from an MPA from Harvard University, he also has graduate degrees in Political Theory, Human Rights Law, Management, and Economics.