Intersectionality of race and gender in Hiring Algorithms
By: K. Tejas
Tags: Hiring, Algorithmic Bias, Bias, Gender, Race

The internet has caused macrostructural changes in labor markets, institutional norms, and corporate organization with workers’ experience of the labor process, their investment in it, and their outcomes from it (Kalleberg, 1989). Hiring technologies have been evolving along with the internet. Internet services like Monster Jobs and search engines became the source for job listings because of their cheaper rates and greater outreach because their room to extend unlike newspaper classifieds. This then followed with online job applications, triggering a jump in the volume of applications for open positions as it became easier to apply for multiple jobs. This came with a price — employers now had to adopt applicant tracking systems to help organize and evaluate rapidly growing pools of candidates.

As the quantity of potential job candidates boomed further to include both higher volumes, some employers began turning to new screening tools to keep up. The conventional assessment would mean deviated team, resources and time. This gave rise to Hiring Algorithm vendors to increase efficiency, and in hopes that they will find more successful and diverse set of employees. With the outcry for Diversity and Inclusion, some vendors are exclusively catering the hiring process based on the diversity principle with promises to remove certain biases.

These hiring algorithms boast about it’s potential to remove bias from the hiring process. They argue that by making hiring more consistent and efficient, recruiters will be empowered to make fairer and more holistic hiring decisions but it is important to realise that what they promise is the interpersonal human prejudice, which is just one source of bias. Other biases like Institutional, structural, and other forms are equally important which are completely neglected in this case. Institutional bias in most cases refers to racial and sexual biases. Structural bias observes broader patterns of disadvantage stemming from contemporary and historical legacies such as race, unequal economic opportunity, and segregation.

When screening systems aim to replicate an employer’s prior hiring decisions, the resulting model will likely reflect prior social biases. Although it might seem natural for screening tools to consider previous hiring decisions, those decisions often reflect the very patterns many employers are actively trying to change through diversity and inclusion initiatives. Hiring is never a single huge decision but a series of smaller, sequential decisions. Siri Uotila, a research fellow at the Women and Public Policy Program at the ‎Harvard Kennedy School says “language is gendered” and algorithms tend to pick these differences as features for selection. While automated phone interviews are “blind”, humans as well as algorithms can still infer information about a person’s race and other demographic factors, such as socioeconomic status, from the sound of their voice.

Researchers from Carnegie Mellon University and the International Computer Science Institute say that even before hiring technologies there is a considerable amount of bias in ad services for these jobs. They found that fake Web users believed by Google to be male job seekers were much more likely than equivalent female job seekers to be shown a pair of ads for high-paying executive jobs when they later visited a news website. This is one subtle way that algorithmic advertising perpetuates an already existing gender divide in the job market. In another paper by Le Chen et. al., they conclude that even when controlling for all other visible candidate features, there is a slight penalty against feminine candidates large enough that masculine candidates receive a substantive increase in rank.

#BlackLivesMatter campaign and other recent events have made the reality of racial and gender based discrimination in our society painfully clear. In one famous experiment, job applicants with white-sounding names, such as Emily, received 50 percent more callbacks than those with African American–sounding names, such as Lakisha.

Sometimes this differentiation goes beyond just race and gender of an individual. Neighborhood, sensitivity of the job role, education of parents, socio-economic status, name of the applicant etc. Living in a wealthier (or more educated or Whiter) neighborhood increases callback rates. But, interestingly, African-Americans are not helped more than Whites by living in a “better” neighborhood.

Vendors have rolled out some promising features that reflect at least some awareness of the deep and systemic inequalities that continue to distort hiring dynamics. Measures like these could ultimately help pull hiring technologies in a more constructive direction, but much more work is needed. Vendors are rapidly releasing new features, and addressing flaws. Our hope is that by using detailed and specific examples to examine the equities and biases of predictive hiring products, we have highlighted common issues that remain unaddressed and unresolved — despite others’ calls for care and caution. There is an urge for advocates, lawmakers, employers, and vendors to confront the emerging questions posed by predictive hiring technologies, articulate principles for their responsible use, and take concrete steps to update regulatory frameworks accordingly.

REFERENCES: