Fair Practice in Recruitment

Historically, recruitment has relied heavily on recruiters reading people’s applications. The problems with this are two-fold: firstly, for a job advertisement that receives hundreds of applications, the main issue is the time it takes for recruitment staff to read all the applications. Indeed, it is understandable that recruitment staff in this position may simply read the first fifty applications they receive, and disregard the rest as a way of making the process more efficient. Of course, this does not make recruitment more efficient - it just makes it faster and easier for the recruiters. Secondly, there is a well-acknowledged problem of bias. When you have people reading applications, it is not just the information on the applicants’ CV that is taken into account: the way they write, the font in which they choose to type, and even the connotations of the applicants’ names have an influence on the recruiter’s decision making. The capacity for discrimination is enormous. Research from the British Academy demonstrates that this is the case.

Thirdly, there is the issue that relates to the role recruiters play as a mediator between applicants and potential employers. Recruiters - although charged with getting the best possible candidate for a given role - are often more concerned with getting as much work as possible, getting it done quickly, and being paid for their work. This is not a good combination for employers or job seekers, as recruiters care more about getting a candidate for the role, rather than the candidate, as it were.

The impact of this way of doing things is obvious: people - and employers - lose out, and it’s mostly due to recruiters cutting corners to save time. An article from Undercover Recruiter outlines the extent of the problem: the average amount of time spent reading a CV is approximately 5-7 seconds. The odds of a recruiter actually reading your cover letter - which are often time consuming and annoying to write as they have to be tailored to each role for which you are applying - is only 17%. And intriguingly, the rejection rate for applications which have a picture of the applicant on them is 88%.

What does all this tell us about recruiting practises? It shows us that recruiters don’t take much time reading individual applications, but more broadly, it shows us the attitude of recruiters. That is, the possibility that recruiters aren’t looking for the right person for the role, so much as they are looking for a reason to reject applications to whittle down the pool of applicants. And after all, who can blame them? The classic system of providing a CV and cover letter isn’t granular or precise enough to give an accurate impression of whether or not someone would be suited for a given role.

As a way of combating the problems of recruiter bias and the time-consuming nature of candidate processing, the recruitment industry is moving towards new methods of recruiting practice. Many see algorithms and Artificial Intelligence as a paradigm shift in how recruitment works - at least the initial stages of the recruitment process. These systems would allow recruiters to skim off candidates who are unsuitable for the role in question by scanning their applications for keywords, and by searching for spelling mistakes. Such AI based-systems would slash the amount of time a recruiter spends trawling through applications, and would enable recruiters to spend more time evaluating the best candidates.

Of course, AI and recruitment analytics brings with it a host of new problems and biases. Although AI can’t be biased for the same reasons as a human operator, the use of AI software is still susceptible to forms of bias. For instance, AI programs have been shown to discriminate between applicants based on their racial, national or cultural backgrounds much like humans, as the World Economic Forum agrees. Furthermore, there have been a great many disasters attributed to AI software making decisions about candidates. An Amazon AI tool used in recruitment famously recommended only men for roles. This occurred because the AI tool was basing its decisions on ten years’ worth of applications to Amazon, the vast majority of which were made by men, and the authors of the AI failed to take this into account. What becomes clear the more you look into AI recruitment technology is that it is of limited utility at present, and that it is susceptible to the same biases as the information with which it was programmed.

In general, the problem with the recruitment industry is that it doesn’t seem to care about finding the right job for the right person as much as it cares about finding the easiest and fastest way to shortlist applicants for their clients. In a way, this makes perfect sense: recruiters get paid when they put forward a suitable candidate for the job, but the goal of finding the right person for the right job is ultimately a secondary concern.

How is this problem going to be fixed? How can employers and applicants get the best results from recruitment? The key is objective representation of individuals. If there were an objective, universal framework through which individuals could represent themselves and their professional/educational reputations, the role of recruiters would not only be far easier but far more accurate. Instead of individuals being viewed as CVs and cover letters to be eliminated from the pile, they could be viewed as people. As people with their own skills and shortcomings, their successes and failures. This is what we are building at TiiQu - a system that allows people to regain control over their professional and personal development by taking ownership of their reputations.

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