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Introduction
Personnel selection is a challenging task: the right employee can make it or break it for the company. Recruitment mistakes can cost a company thousands of dollars a year, and the effects of hiring the wrong person may prove to be long-lasting. It goes without saying that the hiring process is time-consuming, among other things. Applications are typically processed manually, which overloads hiring managers. In the last few years, with the rise of machine learning and artificial intelligence, hiring algorithms have been gaining quite a lot of traction. Such algorithms vary: as Bogen explains, different technologies are applied at different stages of the hiring process. Some of them manage target advertisements to make sure that the right candidates see information about open positions. Others parse resumes to retrieve the key facts and make predictions whether its senders would be a good fit for the company. Since the algorithms impact employment, which is a large part of almost any persons life, there is a moral aspect to the question. This essay proves that from the utilitarian perspective, hiring algorithms are completely unproblematic.
The Utilitarian Value of Hiring Algorithms
The traditional theory of utilitarianism is authored by Jeremy Bentham. Bentham sought to develop a framework for making objective value judgments. Such objective value judgments would ideally serve as a foundation for public policy and legislation because they would be universally acceptable (Velasquez and Velasquez 72). To overcome the subjectivity of morality, Bentham stated that any phenomenon would have to be analyzed by understanding its potential positive and negative outcomes (Velasquez and Velasquez 72). Eventually, for every situation, it would be possible to calculate the net impact as in whether the advantages outweigh the disadvantages or vice versa. Therefore, the right action would be the one that produces more utility than any other potential solution (Velasquez and Velasquez 73). Utilitarianism has grown to be an attractive theory because it appeals to the human need for efficiency and convenience.
The utilitarian analysis of a situation requires considering all the alternative courses of action and contrasting them against each other. The alternative to hiring algorithms is the traditional hiring process that includes posting job advertisements, reading resumes, and selecting candidates without automatization. The first comparison criterion to be taken into account is the number of people targeted by hiring algorithms if they overtake job advertisements. The human resources department can only reach out to so many candidates, even if they know exactly where and what to look for. Besides, they subtract a lot of time from their workday to propel the hiring process manually, which could be distracting them from other tasks of equal importance.
On the other hand, powered by machine learning or artificial intelligence, a hiring algorithm can utilize a continuous flow of data to adjust advertisement settings. The net positive impact, in this case, is a bigger pool of candidates, which gives the hiring company greater freedom of choice. Secondly, the candidates that might not have been otherwise picked by human selectors will receive a chance to learn about an open position and apply. As a result, the employer will have a better chance of meeting the employee and vice versa.
The second criterion is the efficiency of response as compared to traditional methods. Hiring algorithms accelerate the process for both the employer and the employee. Companies are able to make a quick decision whether a candidate is a contender or not, allowing them to only respond to selected individuals. Candidates will receive a quick answer about whether they qualify for the next stage of recruitment. On the contrary, traditional hiring is associated with a lot of uncertainty for candidates and delayed responses, which wastes everyones time. The net positive effect, in this case, is better planning and time management on both sides.
A counterargument to the first point would be that hiring algorithms do routinely leave out some groups of people, replicating human bias. For instance, Bogen recites a study she conducted together with colleagues at Northeastern University and USC. They discovered that an algorithm responsible for job advertisement was not exactly reaching out to people who would be a good fit for a specific role. Instead, it was targeting those demographics that were more likely to click on the link. As a result, Bogen et al. found out that Facebook advertisements of supermarket jobs were mostly shown to women (85% of all viewers) while taxi companies were advertising their jobs to Black people (72%). The researchers concluded that the algorithms were blatantly reproducing the biased preferences of hiring managers.
It is arguable whether the net positive effect of expanding the pool of candidates outweighs the effect of introducing and reinforcing bias that shrinks the said pool. As Sandel notes, moral dilemmas naturally lead to moral reflection (18). Velasquez and Velasquez argue that utilitarianism does not confine itself to the immediate outcomes of action (73). Instead, the utilitarian perspective concerns itself with the long-term effects of a decision. Therefore, utilitarian analysis needs to factor in time to be more rigorous. In the case of hiring algorithms, thinking long-term would yield two possible scenarios, the net effect of which would probably show significant variability.
The first scenario is optimistic: as the world becomes more aware of social justice issues, employers adjust their hiring policies accordingly. Artificial intelligence and machine learning utilize data about hiring decisions that are not motivated by racism, sexism, or other forms of discrimination. Hiring algorithms stop excluding and mistreating women and minorities while retaining their speed, efficiency, and convenience. If this is the case, then from the utilitarian perspective, hiring algorithms provide more value than traditional hiring.
However, in this situation, a more pessimistic scenario is also possible. Stereotypes and prejudice are incredibly persistent, especially in mature people who are more set in their ways. Even though social justice movements have been around for a while, society has yet to make a paradigmatic shift toward equality. Bias is likely to be here to stay, and if it proves true, then human-driven discrimination will be ingrained in the logic of otherwise impartial algorithms. In the long-term, denying people employment opportunities or offering them worse options than they deserve based on superficial selection criteria would have a net negative effect on entire communities. Unemployment and underemployment may mean consequences as bad as crime and generational poverty. To conclude this scenario, it is imaginable how hiring algorithms could yield temporary benefits but prove to be detrimental to the job market and social causes in the long run.
From the perspective of justice, hiring algorithms prove to be even more faulty than from the utilitarian perspective. An obvious pitfall would be denying worthy individuals job opportunities, which is far from fair. Building on the findings made by Bogen, it is safe to say that hiring algorithms compromise the equality of opportunity. A less obvious point would be not the impact on the victims but the intention of the doer (Sandel 18). Sandel is convinced that moral judgment should also concern the motives of the one taking action. A company that dismisses bias and keeps using hiring algorithms cannot be socially responsible. Not only does it harm people within its own scope of effect, but it also sets a precedent for others, normalizing such work practices.
Conclusion
Hiring algorithms emerged several years ago as a response to one of the most challenging problems of human resource development: recruitment and selection. While not being universally applied just yet, at some companies, hiring algorithms are already helping with some of the aspects of the hiring process. The issue of hiring algorithms is not devoid of controversies. Utilitarianism provides a framework for making value judgments depending on the goodness of outcome for all. At first glance, hiring algorithms outdo its main contender: traditional hiring. However, further utilitarian analysis of hiring algorithms proves that the issue is not as straightforward as one would expect. Depending on the status of social justice, hiring algorithms may add more value as they would no longer factor in human bias. Alternatively, if social prejudice persists, hiring algorithms will wreak havoc on the job market by excluding and discriminating against workers.
Works Cited
Birch, Kean, et al. Business and Society: A Critical Introduction. Zed Books Ltd., 2017.
Bogen, Miranda. All the Ways Hiring Algorithms Can Introduce Bias . Harvard Business Review, 2019, Web.
Braun, Josh. The Devil in the Details: User Tracking Is Hurting More Than Our Privacy, Its Doing Serious Damage to Public-Interest Media, Too. 2019. Web.
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