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AI In Recruitment

AI in Recruitment

It is increasingly common to hear HR Tech and Assessment companies talk about their AI features. They may even tout or promote those credentials prominently in their marketing. By AI, of course, we mean Artificial Intelligence.

According to Techopedia, Artificial Intelligence (also known as machine intelligence) is a branch of computer science that aims to imbue software with the ability to analyze its environment using either predetermined rules and search algorithms, or pattern recognizing machine learning models, and then make decisions based on those analyses. In this way, AI attempts to mimic biological intelligence to allow the software application or system to act with varying degrees of autonomy, thereby reducing manual human intervention for a wide range of functions. A key principle here is that they can operate, learn, and solve problems independent of humans.

AI in Recruitment is unclear

But what exactly does AI mean in Recruitment? And does true AI really exist in recruitment at this point in time? Perhaps? But the waters are a little muddy. Machine Learning, Automation, Algorithms, Predictive Data, can these AI designs really enable increased efficiencies and better decision-making for organizations and their increasingly critical recruitment process as their marketing campaigns suggest?

When we start thinking about machines making hiring decisions, there are a multitude of emotional responses. Some of us are excited by the prospect of increasing predictive accuracy and reducing bias. For others it brings a dystopian fear that the machines will take all our jobs. It is worth asking an important question: Exactly what aspects involve artificial intelligence, and do they have direct oversight into the factors that have been identified as predictive?

Computer Says No

One of the big concerns about AI is a lack of transparency, that the computer says "no" to candidates for reasons unclear. There was a well-publicized story in 2018 about Amazon developing a recruitment AI that independently decided that being male was a predictive factor. Amazon fed thousands of candidate and final hire resumes from the past 10 years for technical developer roles into their machine learning AI system. The technical roles had predominantly been filled by males in the past, therefore the AI decided this was a key factor. Not only are resumes a relatively poor predictor of job performance, a key point here is that the factor identified by the AI was unknown to the recruiters. It is crucial to have human oversight when determining eligibility and key performance factors for an individual job to ensure face validity and avoid terrible errors like the above example.

How Does Harrison Use Elements of AI?

At Harrison Assessments we understand that all employment is based on mutually beneficial relationships that begin with the candidate experience. Not only does our technology shorten the assessment process, but it also reduces the cost and time to hire. We can quickly identify, interview, attract, and hire the best candidates with Predictive Analytics.

Harrison's recruitment technology pre-screens applicants for qualifications and job specific behavior. The process is Automated to reduce human bias and increase efficiency, while being designed to "screen in" candidates who fit the Eligibility Requirements (experience, skills, qualifications), the Suitability Requirements (behavioral fit for the job), and if applicable, the Cognitive Requirements (problem solving ability sufficient for the job).

These Job Success Formulas (JSFs) are weighted Algorithms which many would consider to be a core component of AI. This underlying concept of "screening candidates in" (as opposed to the more orthodox "screening out") is crucial to diversity and inclusion efforts as it focuses only on the skills and behaviors that increase the likelihood of candidate success in that specific job. The candidate scores for each element are clearly displayed and you can drill down further into the specific components of that score for each individual.

To further customize a Job Success Formula to your organization's actual performance data and distinct job requirements, our Benchmarking solution uses Machine Learning to identify the predictive factors for success. The results are carefully reviewed by Harrison experts to ensure face validity, and we can review and fine-tune these algorithms over time. Harrison's world-wide research has shown that job success behaviors are unique to specific jobs and that general personality factors alone cannot be used to effectively predict job specific success.

Despite elements of AI being present, we don't necessarily present Harrison Assessments as an AI enterprise. Our focus is on enhancing the hiring process for organizations to better understand the candidate's attitudes, motivators, work values, and so much more. Armed with this information, managers can facilitate conversations during the onboarding period that matter, and that result in successful, mutually beneficial relationships and employee satisfaction and retention.

Legal Compliance

It is essential that any psychometric assessment used in a recruitment context is fit for purpose. Our assessments comply with EEOC regulations as well as ISO 10667 for Job Specific Assessment. Our Benchmarking Analytics option provides job specific research and scientific validation for your specific custom criteria. We've been doing this for over 30 years and you know what? We've got pretty good at it.

So when you are researching new HR tech, be sure to look beyond the marketing and ask how is AI actually being used to enhance insight and efficiency.

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