A job rejection usually arrives in the form of a few carefully worded sentences. A thank you for your interest, a notice that the company has decided to move in a different direction, perhaps an assessment that the candidate is not the right culture fit. The Reject the Rejections campaign takes that familiar language and turns it back on the system that produces it.
British nonprofit People Like Us, which advocates for ethnic minority equity in the workplace, together with creative agency Worth Your While, has launched a campaign that looks at the issue of automated hiring from the perspective of people who often do not even know whether another person has ever seen their application.
At its centre is the four-minute film Dear [Name], in which Ebenezer Gyau, an actor known for the series Amandaland and Black Mirror, recites lines taken from real rejection messages sent to ethnic minority candidates.
The film was directed by Amara Abbas through production company new-land. Gyau is positioned in an almost empty waiting room, in front of a two-way mirror behind which sits a team of white employees. As familiar rejection phrases follow one another, from the decision to “move in a different direction” to the message that a candidate is “not quite the right culture fit for the team”, the formal language gradually gives way to frustration with a system that makes decisions about people.
Other ethnic minority candidates are also in the room. The wait ends with a phone call announcing that the only white candidate has been given the job, after which she crosses to the other side of the mirror and joins the employees.
The film, however, is not conceived as an isolated metaphor.
The new research underpinning the campaign shows just how much automation has already become part of the job-seeking experience in the UK. Research conducted for People Like Us by Censuswide included 2,000 job seekers. Among respondents who had been rejected for a job in the previous two years, 74 percent suspected that at least one decision had been made automatically, without a human reviewing their application. At the same time, only 15 percent of candidates said an employer had informed them that an automated system was being used.
The difference can also be seen in the way candidates try to navigate such processes. According to the research, 70 percent of ethnically diverse candidates had removed or concealed some part of their identity from their CV, compared with 57 percent of white candidates. Other data cited alongside the campaign also shows that ethnic minority candidates were more likely to be rejected within the first hour of applying, at 41 percent compared with 32 percent of white candidates.
Rather than presenting automation as a problem that can be solved simply by returning to entirely manual processes, the campaign focuses on transparency and oversight of how the technology is used.
“Employers aren’t the villains here. Most resort to automation with good intentions to cope with the growing volume of applications, and four in ten do so believing they are reducing bias,” said Sheeraz Gulsher, co-founder of People Like Us. He added that good intentions are not an adequate substitute for oversight, particularly when employers that checked their systems found differences in outcomes based on ethnicity.
The campaign therefore also gives candidates a practical way to challenge a decision. In collaboration with Planes Studio, a tool has been developed through which rejected candidates can contact a prospective employer, ask whether automation played a role in their rejection and, where applicable under UK data protection law, request a human review of the decision.
People Like Us is also calling on employers to openly disclose whether they use automated systems to screen candidates and to conduct independent audits of their potential bias, while calling on the government to consider additional transparency requirements. In this way, Reject the Rejections does not reduce the debate to whether technology should be part of hiring, but instead directs attention to what happens when a decision becomes faster than a candidate’s ability to understand who, or what, actually made it.
