Gender Equity and Recruitment: the Good, the Bad and the Algorithmly
By Anna Daly
In the decades since Germaine Greer wrote The Female Eunuch and Aileen Moreton-Robinson wrote Talkin’ Up to the White Woman, women from a range of backgrounds have become more visible in the public domain and increasingly recognised for their roles in shaping the world. This is no less true of the workforce, where women are now employed across a wider variety of sectors and roles. Yet gender discrimination persists and has an impact on every aspect of employment, right down to the job application process. To make things more frustrating, the reasons for discrimination are highly complex. While stereotypical assumptions about gender still have a sizeable impact on a candidate’s likelihood of success, what those assumptions are and how they come into play differ dramatically across regions, sectors and roles. We can’t hope to even list all the factors leading to gender discrimination here but we can leave you with some food for thought based on findings from recent studies in the area.
Gender Stereotypes Persist
It is perhaps not surprising that studies carried out in the 1980s and 1990s found managers preferring men over equally qualified and experienced women for managerial and leadership positions and that this preference was based on stereotypical assumptions about gender: centrally, that “masculine” traits were critical to professional success and possessed only by men. It is also not surprising that things have changed since the 1980s and 1990s, most notably the law. Australia has quite far-reaching laws that make it illegal to discriminate on the basis of ethnicity, age, disability or gender. Translating that legislation into meaningful change has proven an altogether trickier task. Indeed, gender discrimination is still prevalent, despite increasing numbers of employers actively seeking to address the issue. With a complex network of employers, recruitment professionals, advertisers, employees and job applicants operating across a range of sectors and roles, the chances of finding one surefire way to combat discrimination at the recruitment stage are pretty slim.
In the wake of research uncovering biases of both the conscious and unconscious varieties, employers and recruitment professionals have tended to recommend that applicants anonymise resumes and cover letters as much as possible. That means removing explicit references to age, ethnicity, gender and physical attributes, as well as inadvertent references, such as noting the year when secondary or post-secondary education was completed. The reasoning behind this advice is sound since anonymising application materials compels both candidates and employers to focus on skills and experience rather than assumptions about age, gender and ethnicity. However, a 2017 study also suggests that, faced with the absence of personal identifiers, employers may well spend time trying to figure out the gender, age, ethnicity and physical attributes of a prospective employee anyway. Prompted by the pressure to appoint the right person to the role, employers choosing from an equally skilled pool of applicants may look harder for cues about identity to help negotiate the wider uncertainties surrounding the recruitment process.
What about algorithms?
Recruitment algorithms do not care – at least not yet – about the uncertainties surrounding recruitment. They simply match skills and experience with what any given role calls for, and rank applications accordingly. At least this is the pitch from the denizens of Silicon Valley and its ilk. If only this were true.
In 2017, in-house engineers designed an algorithm trained on 10 years’ worth of resumes from people applying for jobs at Amazon to identify the skills and experience best suited to roles at the company. Unfortunately, because the majority of historically successful applicants were men, the system taught itself to downgrade resumes containing the word “women’s”; assigned lower scores to graduates of two women-only colleges; and assigned resumes using words such as “captured” and “executed” (purportedly used more commonly in applications from male candidates) a higher rank. Technology companies big and small may claim to be designing better (read: more objective) algorithms but without the objective data on which to train those programmes, the results will continue to be problematic.
Where does this leave us?
There is no failsafe way to ensure that applications submitted by identifiably female candidates will be assessed accurately or fairly. Many employers do want to address gender discrimination however and, with the greater numbers of women taking up influential roles across the workforce, some of the deeply ingrained attitudes that perpetuate discrimination seem likely to change. In the meantime, give yourself the best chance you can. Demonstrate that you take the application process seriously by ensuring your resume and cover letter are articulate and error-free and; allow your relevant skills and experience to sing your praises as loudly as any perceived notions about identity.
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