Predictive hiring assessment: AI says you are hired

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  Quantumrun Foresight
• September 12, 2022

Insight summary

Artificial intelligence (AI) is reshaping recruitment by using data to identify top candidates, reducing bias and increasing workplace diversity. These automated systems streamline hiring processes, potentially boosting company efficiency and profitability while offering candidates a more personalized experience. However, the reliance on algorithms raises questions about fairness and the need for government regulation to ensure ethical use in the labor market.

Predictive hiring assessment context

The Great Resignation has shown society how a black swan event can change the employment market overnight. Companies have adapted by doubling down on hiring the best available professionals. To reduce uncertainty and accelerate the hiring process, employers are using AI-powered assessment and recruitment platforms that leverage predictive data.

Even before the rise of big data and AI, many companies had already begun using predictive hiring techniques, albeit manually. These techniques narrowed down the characteristics that had historically provided a pool of high-quality candidates for an open role, including tenure in previous jobs, educational background, and core skillsets. However, this manual procedure can be highly subjective, inaccurate, and create dissonance among hiring managers and recruitment teams.

Predictive hiring and talent identification tools, supported by AI, can analyze thousands of CVs daily, searching for specific keywords and patterns that help identify candidates best suited for a role. Every piece of information a job candidate provides can be quantified and analyzed, including job knowledge, age, average job tenure, personality, language skills, and previous experience. Artificial intelligence chatbots are also used to conduct certain first phases of the interview process, freeing human recruitment teams to focus on higher-value tasks. 

Disruptive impact

The integration of automated assessment tools in recruitment processes aims to minimize cognitive and unconscious biases, potentially enhancing diversity and inclusivity in the workplace. By relying on algorithms to evaluate candidates, employers can focus on applicants' skills and qualifications rather than external factors like educational background, wealth, race, gender, or age. This shift towards a more objective recruitment approach can lead to a broader and more diverse talent pool, as candidates who might have been previously overlooked due to superficial reasons are now given equal consideration. Additionally, the automation of certain interview components, such as cognitive screening and introductory interviews, streamlines the process, allowing for more efficient candidate evaluation.

The long-term adoption of predictive and automated hiring systems may result in significant benefits for employers, including increased internal efficiencies and reduced hiring costs. By consistently recruiting higher quality candidates, companies can enhance their overall productivity and profitability. Furthermore, the ability of these systems to adjust offered pay in real time based on applicant feedback and efficiently source necessary documentation can optimize the hiring process. This method also fosters a more positive applicant experience, potentially increasing the attractiveness of the employer in the job market. 

Automated hiring systems could lead to a more equitable labor market, with diverse and inclusive workforces contributing to broader societal benefits, such as reduced income disparities and enhanced social cohesion. However, there may be concerns about the reliance on algorithms, such as potential biases in the programming or the exclusion of candidates who do not fit within the defined parameters of these systems. Governments may need to implement regulations and guidelines to ensure these technologies are used ethically and effectively, balancing the need for innovation with the protection of workers' rights and interests. 

Implications of predictive hiring tools 

Wider implications of the hiring process becoming increasingly automated may include:

• The use of chatbots to conduct preliminary interviews and remote tests and provide 24/7 support for candidates throughout the entire recruitment process. 
• A customized experience for candidates, including giving real-time status updates on their application and post-interview feedback.
• Increased allocation to HR technology budgets to fast-track the hiring process and build an updated pool of potential candidates for future roles.
• Applicants adapting their job hunting and interview approaches to appeal to algorithms instead of humans.
• The potential for older workers to be indirectly discriminated against in the labor market if they lack the digital skills to interview effectively during the automated stages of a recruitment process.
• Instances of negative press should a recruitment algorithm be proven to display a bias toward one group of applicants over another.
• Public pressure on state/provincial and federal governments to regulate the extent that private sector companies can use automated recruitment solutions.

Questions to consider

• Do you think data systems can accurately predict the compatibility of potential candidates with the role and the company?
• How else do you think automated assessment tools can change how companies hire in the future?