The use of AI and Algorithms in the Recruitment Process

Although not from an academic perspective, Dormehl (2014) argued that algorithms are ultimately devised by humans and are therefore inherently vulnerable to human prejudices and biases when they are written. It can be seen that the increasing use of technology in the workplace has also meant that there is an increasing use of technology to assist employers, recruiters and managers to make decisions about who to employ. A notable feature change to recruitment and selection has arisen as a consequence of the introduction of technologies which employers utilise and algorithms to rank candidates based on various job and sometimes non job related criteria.

The use of applications from organisations such as HireVue by organisations such as Unilver and Goldman Sachs give the impression that screening and ranking apps being commercially sensible activity (Feloni, 2017). Whilst Feloni is not critical of the activity, he raises some interesting questions regarding the potential for bias in the employment of applications such as HireVUe. HireVue is by no means the only application available on the market, however, it appears that the organisation provide support and guidance to employers attempting to remove bias form the hire process. They at least signal the potential for this to be problem and acknowledge that

employer must work hard to ensure that they are conscious aware of potential biases prior to setting criteria. This is not something which is offered by all application

providers. Pymetrics (Pymetrics.com, 2018) for example claim that their application removes even the need for employers to undertake this self-disclosure, they are than confident they have produced an algorithm so sophisticated, it is not required. Harver claims to be the global lead in the use of AI within the recruitment process and insist the eradication of CV’s will lead employers to making better less discriminatory hiring

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decisions (Academic On File, 2018) . This is a sizable claim given that the

technology enabling this has not been in use for a protracted period of time and thus the longitudinal outcomes can only be theorised.

Chan and Wang (2018) discussed the increasing use of information gathered from online sources being utilised in hiring decisions. Although there research focused on the tradition method of gathering evidence from online sources, they raise an interesting questions regarding whether the decision mechanisms utilised in the online world are comparable to that of the offline world. Further Acquisit and Fong (2015) argued that recruiter behaviours were altered as a consequence of information gleaned from online sources. This presents a unique challenge and opportunity for employers to reveal to candidates that they in in fact utilise online information to make hiring decisions, but that they ensure their algorithms minimise the potential for bias towards the information that is found.

Another significant use of algorithms in recruitment can be found in the data mining of prospective candidates information which might be contained on platforms such as LinkedIn. Utlising clustering techniques, employers could be inadvertently marginalising whole sections of the labour market without even realising it (Shehu and Besimi, 2018). They also discuss the employment of data warehouses by employers as being potential both problematic and very useful. The storing of such great volumes of information might provide an opportunity for an employer to make a better hiring decision, but once again this is only possible if the variables in the algorithm are unbiased. Notwithstanding the potential negatives associated with the use of technology in data mining, candidate screening activities and possible biases, there is scope for greater candidate diversity in this regard. The

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ability of employers to view candidate’s information without the including of personal

data such as names, age, sex, sexual orientation and gender can help enable this. Bohnert and Ross (2010, p.342) discuss non-verbal cues which may arise from reviewing candidates social networking sites even at recruitment stage. For

example, if as is the case with some organisations (eg Eversheds), those potential

applicants/candidates are encouraged to join employer controlled recruitment groups. There is a danger that those responsible for making recruitment decisions may have unfettered access to photographs and platform content of the applicants thus potentially revealing personal attributes, such as one example – perceived physical attractiveness. Using Wiener’s (1993) Attribute Theory, it is evident the

content and photographs on social media platforms could have an effect on an employer’s judgements about the applicant. He theorised pictures depicting users in

a drunken stupor had a far lower rating, by decision makers, than those pictures taken in a professional or familial setting. While the employer might not be openly or consciously seeking information about the applicant, by default they may be exposed to it. There is also a risk of “similar-to-me” bias, which those responsible for

overseeing the process need to be aware of and try to mitigate and while Bohnert and Ross’s paper is useful in terms of determining the influence of social networking

sites on candidate evaluation, it does so from a purely qualitative perspective. In order to fully understand how employers can mitigate bias in the use of social media for recruitment there appears to be a gap in the knowledge in terms of a

substantively qualitative focus, which this inquiry attempts to address.