The use of AI in the recruitment process has led to controversy regarding its ability to promote gender equality or increase diverse talent.
On the one hand, some perceive AI as the core cause of discrimination and bias, due to its essential technical fallacies and mimicking of human biases. In other cases, there might not be sufficient training data for several social groups. To exemplify, studies have shown that facial recognition technologies might discriminate against black women, compared to white males. From an HR perspective, one may mention the alleged gender disinclination of Amazon’s recruiting tool, due to biased training datasets.
On the other hand, however, some may suggest that traditional recruitment methods have been digitally transformed, particularly highlighting AI usage to automate hiring and increase equality, diversity, and inclusion (EDI) across companies. Besides the promise of greater operational efficiency, hiring managers and recruiters can moderate the impact of existing sociocultural biases using AI-driven debiasing tools.
Nonetheless, the adoption of AI as a recruitment tool upholds intrinsic loopholes connected to its human creators. Below, we will consider the opportunities for AI-driven hiring, the debiasing tools on the market, and AI fallacies in the development of EDI products.
AI in diverse recruitment
Increased digitation processes and global crises have transformed traditional recruitment methods. Crucially, the Covid-19 pandemic has accelerated a broad unemployment crisis, due to the partial closure of businesses and traditional professions. As in other disciplines, this crisis has involved notable societal implications on minority groups as their employment opportunities were diminished due to the exacerbation of sociocultural biases in hiring.
Therefore, HRTech companies have accelerated the development of AI-driven tools to increase diverse talent and enhance the hiring capacity and volume, as well as candidate matching per requisition. Many will observe the increased scrutiny attributed to EDI as a disrupting trend in recruitment processes across sectors, attempting to increase representation in the full candidate funnel from candidate application, screening, interview, and offer stages.
How well do you really know your competitors?
Access the most comprehensive Company Profiles on the market, powered by GlobalData. Save hours of research. Gain competitive edge.
Your download email will arrive shortly
Not ready to buy yet? Download a free sample
We are confident about the unique quality of our Company Profiles. However, we want you to make the most beneficial decision for your business, so we offer a free sample that you can download by submitting the below formBy GlobalData
Leading debiasing tools
The HRTech market offers a plethora of AI-driven tools and applications, aimed at mitigating gender and ethnic biases in recruitment. For instance, various tech start-ups, such as Eightfold AI, HiredScore, and HireVue offer masking tools, hiding candidates’ details from recruiters until the interview stage.
This can be added to the creation of tailored diversity candidate pools and campaigns in internal recruitment datasets. While these tools diversify the candidate pools until the interview stage, this does not guarantee the hiring of diverse candidates. Therefore, other services offer additional screening sources and skill-matching platforms such as anonymized skill assessments and questionnaires for candidates.
In addition to the above, other companies have developed AI-driven matching tools, ranking candidates’ relevance to a specific requisition based on the job description’s requirements and previous hiring decisions. This enables hiring managers to promote diverse and relevant talent per position, as well as mitigate the psychological and social barriers of minority candidates from applying and the fear of rejection.
AI driven tools
Despite the promise to increase diverse talent in companies, there are notable AI loopholes that should be addressed. For instance, AI is still mainly developed by human developers, and previous hiring decisions and datasets may still reflect gender and ethnic biases in candidate screening.
Therefore, AI-driven debiasing tools still require statistical fixes and modifications to cope with this limitation. Moreover, the new recruitment methods might impact those who are not as digitally oriented and accentuate the digital divide due to cross-generational and sociocultural factors.
While there are tremendous opportunities for AI in recruitment, as it contributes to the hiring of diverse talent and the mitigation of sociocultural biases, the human origin of AI-driven products does not guarantee a complete debiasing process. Therefore, greater scrutiny should be attributed to reviewing the AI outputs of such tools, as well as increasing the digital skillset of future candidates.