Gender bias in the Online Job Search

There are many stages to the hiring process, and algorithms are involved in almost all of them.

Gender bias refers to the “unfair difference” in the way that both men and women are treated . In the context of the online job search, this refers to the advantage male job seekers have versus female job seekers. The advancement of technology and big data has led to companies recruiting and hiring potential employees through the use of big data and artificial intelligence . The use of algorithms in the job search process can perpetuate existing biases which raise ethical concerns regarding opportunities for females being blocked, and how systematic gender roles are disadvantaging female job seekers.

Evidence of Bias

Job Recruitment Algorithms

Job recruitment algorithms have been found to reinforce and perpetuate unconscious human gender bias . Because job recruitment algorithms are trained on real-world data – and the real world is biased – the algorithms amplify this bias on a larger scale. Decision-making algorithms are “designed to mimic how a human would…choose a potential employee” and without careful consideration, algorithms can intensify bias in recruiting (4).

Utilizing real-world data to shape algorithms leads to algorithms producing biased outcomes. Algorithms make predictions by analyzing past data (2). If the past data includes biased judgments, then the algorithm’s predictions will also be biased. Facebook’s tool called “lookalike audience” allows advertisers – in this case, employers – to input a “source audience” that will dictate who Facebook advertises jobs to, based on a person’s similarities with this “source audience” (2). This tool is meant to help employers predict which users are most likely to apply for jobs. If an employer provides the lookalike tool with a dataset that does not include a lot of women, then Facebook will not advertise the job to women. Employers might use this tool to deliberately exclude certain groups, but there is also a possibility for employers to be unaware of the bias of their “source audience” (2).