AI: Hiring Without Any Discriminations or Frontiers

The American population has been growing more diverse for some time now. Women have and will continue to represent a large swath of the educated working population. And the younger educated working population is increasingly made up of people of color. Yet companies are struggling when it comes to hiring a workforce that reflects our changing populace. But the emergence of artificially intelligent technology has shown potential when it comes to creating a recruitment environment that fosters diversity in the applicant pool.


Diversity in the workplace

Diversity in the workplace is important for a variety of reasons that benefit both employees and employers. Working alongside individuals of different ethnic backgrounds, genders, ages, disabilities, etc. helps to create and environment that fosters mutual respect amongst employees. When working alongside different kinds of people, employees’ various talents can be nurtured, and the combination of differing strengths and work styles helps to cultivate teams of people prepped to handle a variety of tasks in more innovative and effective ways. Companies that cultivate a diverse workforce tend to have a more positive and productive working environment with employees that are more capable of resolving conflict in a respectful way. By working with others from varying backgrounds, employees learn to respect others’ differences as well as grow together from their similarities. Companies that demonstrate social responsibility and invest in making their work environment one that is open and supportive of different kinds of people will garner more success and customer loyalty.

AI can help…and hurt workplace diversity

Far too often talented candidates from diverse backgrounds are overlooked, as they do not come from the traditional candidate pools that employers are used to picking from, meaning many skilled individuals miss out on great job opportunities and many companies disregard skilled people that can help their company grow. There have been a few companies that have begun implementing AI powered recruiting software in their hiring process, but this has not been without its issues. In 2014, Amazon, the e-commerce giant, began building an internal computer software designed to review job applications that came through the company’s online hiring portal. The machine would review potential job applicants and give them a ranking between one and five stars. Using the data developed by the machine, human recruiters could then pick through the smaller pool of more desirable candidates to then go on to get hired. The goal of the project was to build a machine capable of screening thousands of resumes in a short amount of time to identify top tier talent quickly. A machine like this would help to eliminate the arduous process of reviewing resumes manually, saving the company time and money. And because it was a machine doing the reviewing, developers also thought that it would make the screening process unbiased. It was supposed to be Amazon’s “holy grail”. But one year later, developers of the machine began to realize that this was not the case. In 2015 developers began to notice a startling pattern with which the machine was ranking the job applicants. The machine would rank men five stars overwhelmingly and rank women lower, even if the women in the pool were just as qualified as their male counterparts. A grave error had been discovered. The job of the machine was to examine resumes and identify patterns in the resumes that matched existing patterns in resumes that had been submitted to the company over a period of ten years, ostensibly looking for resumes than match the kinds of candidates the company tended to hire.

Unfortunately, during that time period, the vast majority of Amazon employees hired were men. The machine did not just learn to distinguish candidates based on skill and experience, it also learned to discriminate against them based on gender. When the machine observed words like ‘women’ or ‘women’s, it automatically gave the resumes with these words a lower score. That meant that women who had gone to an all woman’s college or had participated in a women’s clubs at school were put at a disadvantage regardless of their skill level. Developers tried to alter the machine’s sorting process so as not to mark down candidate profiles with these words, but as the machine was designed to pick candidates similar to those that they hired in the past the machine could still potentially find a way to discriminate based on gender. In 2018 Amazon scrapped the project.

With Amazon’s failed experiment made public, many companies have felt discouraged to use AI in their hiring process. But proponents of AI say that it is absolutely possible to develop an AI powered recruiting system that is fair. When implemented right AI can help overcome human biases. One such person is Genevieve Jurveston, Co-Founder and CMO of Fletcher. Fletcher is an AI-based recruiting firm that Juverston claims can help companies recruit a qualified and diverse group of job candidates. Juverston believes that AI can be a huge asset to bring equality to the hiring process when it is programed correctly. The biggest benefit that AI brings to the table is that is has the potential to operate without human bias. For example, humans have developed a biased towards men in high ranking positions based on the fact that most of us have only ever seen executive positions filled by men. As a result, when recruiting for a high-level position, human recruiters will have a bias towards hiring male candidates for the position. Juverston says that in order to resolve the problem of human bias in the hiring process, humans must pull themselves out of the hiring process, at least initially. Fletcher’s technology is fully automated to screen through candidates to find optimal talent to present to Fletcher’s clientele. Unlike Amazon’s internal recruiting software, Fletcher’s software is designed to search for potential candidates using much more innovative methods.

Many companies screen applicants based on criteria such as whether they went to an Ivy League university, or if they worked for a top tier company. As these institutions tend to be less diverse, the pool of applicants hired using this method are also less diverse. Instead, Fletcher’s software focuses on criteria such as career progression, which is a great indicator of an applicant’s skills and ability to succeed. This method also eliminates the barrier facing candidates that did not attend elite universities or have had a less traditional career trajectory. The pool of candidates that Fletcher presents to its clients is a lot wider and a lot more diverse according to Juverston. And having a more diverse pool of applicants will help to ensure job applicants of varying backgrounds can get hired. If a pool of applicants only has one woman in it or one person of color, human recruiters tend to be biased against that person because they do not fit the norm. If the pool of applicants is diverse, women and people of color have the same chance at being hired as their white male counterparts. Fletcher’s developers keep a close eye on the technology and monitor its outputs to make sure the pools of candidates it is sending to their clients are diverse. Though Juverston wants to automate much of the recruiting process, she does not believe in eliminating human interaction entirely. The organizations that use her service are allowed to sort through and pick from the candidate pool that her machine produces. And human interviews are still very much a necessity in the job hiring process. What AI powered recruiting technology can do is cast a wide net to help companies to find the most qualified applicants amongst a pool of candidates of various backgrounds, identities, and experiences so as to help companies to hire much more fairly.

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