The future of recruitment: Current opportunities and future ethical considerations

The U.S. Department of Labor’s estimate is simple–the average cost of a bad hiring decision is at least 30 percent of the individual’s first-year expected earnings. If you take an employee with an annual income of $50,000, the cost to the organization can be $15,000. Add to this figure the current media coverage of the Great Resignation and labor shortages, and the importance of hiring the best people for any given role is undeniable.

To help companies, candidates, and the public understand how they can use the proven science of industrial organizational psychology to improve hiring and the world of work, we have written a book, The Future of Recruitment: Using the New Science of Talent Analytics to Get Your Hiring Right. We analyze decades of IO psychology research and explain how the latest technologies are being used to improve hiring. We describe how organizations can use automation to improve their recruitment and selection and drive diversity and productivity at the same time.

Below are some tips from the book to help you build the recruiting processes you need for today with the challenges of tomorrow in mind.

Present day opportunities 

There is no way around it, traditional job interviews aren’t properly assessing candidates. Countless studies demonstrate that unstructured interviews (most job interviews) do not predict future job performance. Instead, they are opportunities for hiring managers to project their biases onto candidates and rely on their overconfident intuitions about who would be a good fit for the job or culture. Similarly, candidates are aware that interviews are a performance and seek to inflate their reputation. Overall, this leads to bad hiring decisions and wasted potential.

The opportunity to “get your hiring right” should include leveraging tools of the data science revolution. The latest technological advances make it easy to deploy AI at scale in your candidate journeys. AI-powered tools are affordable and less subjective than people, giving businesses new ways to identify and recruit the best talent while also giving recruiters back time to spend on high-value candidate-centric activities. 

AI not only creates operational efficiencies, it can also create fairer and more predictive talent decisions. Hiring decisions are usually riddled with bias and subjectivity, and carefully tested and developed algorithms can ensure that every candidate is evaluated against the same criteria. These criteria can be tested to ensure that it actually predicts job performance and does not cause adverse impact and discriminate against marginalized and underrepresented groups.

Game-based assessments

In a competitive job market employers are rethinking resume criteria. Does this position really need an advanced degree and ten years of experience? Employers who prioritize skills, not resumes, are poised for success in an increasingly competitive global economy. Psychometric assessments help skills-focused leaders measure attributes extensively documented to predict job performance (such as personality, values and cognitive abilities). These traits have historically been measured using long, boring surveys. Instead, game-based assessments use video game components like interactive puzzles to provide a modern and engaging assessment experience. 

The best part is that these gamified versions are just as accurate as traditional assessments. Studies show that game-based assessments are accurate and reliable measures of critical talents, like emotional intelligence, creativity, and problem solving, and report to be more engaging than alternatives. 

Structured, digital interviews

Standardized job interviews—interviews that have multiple raters, standardized questions, and scoring rubrics—are the gold standard. Studies show that when we control for human bias, interviews become incredibly powerful. Digital, asynchronous interviews offer three significant advantages: 

1. They scale, enabling exponentially more candidates to apply for a role and be fairly evaluated.
2. They standardize decision-making across hiring managers by implementing the same questions and rating categories for all candidates. A candidate’s answers can also be analyzed by natural language processing to predict potential job performance, further supporting hiring manager decision-making. 
3. Adverse impact is easier to identify, measure and mitigate. By quantifying more of the hiring journey, organizations can better recruit diverse talent and build more inclusive workplaces.

An ethical framework for future tools

Anyone who has interacted with the job market for half a second can see that humans have done a terrible job, creating biased processes that aren’t the least bit technical in nature. So as we look to the future and start to think about the implications of technology, our baseline assumption is that technology itself isn’t ethical or unethical, but its outcomes certainly can be. 

We’ve  built a framework to help guide talent leaders when choosing AI-driven technologies:

• Benefits to the job seeker: Technology and AI should be used to raise a candidate’s awareness about their talents, development gaps, and career opportunities. As recruitment becomes more data-driven, organizations have a responsibility to share these insights with applicants.

• Informed consent: AI algorithms can process thousands of data points and be quite challenging to interpret. If AI is to be used in hiring processes, we must share with candidates how their data is being used and provide the opportunity to both give consent and delete their data.

• Confidentiality, anonymity, and data protection: As more data is being collected from candidates, HR departments must level up their data and ethics governance to ensure data is not being misused. This includes data safety, anonymization, and safeguards in the case of privacy breaches.
• Explainability: AI should support, not replace, human decision making. AI has the power to neutralize our biases. However, it can be misused and overleveraged. The wielders of AI algorithms must be able to explain how a user received a particular score or evaluation, be aware of how the algorithm was developed, and factor in its strengths and limitations when forming a hiring decision.

Conclusion

Finding the best person for a job remains the number one goal of recruitment and selection. And while the most in-demand skills and abilities will evolve as automation fundamentally changes large sectors of the economy, ensuring processes are grounded in objectivity and fairness should always stay a constant priority..

For a deeper dive into the possibilities available, order a copy of The Future of Recruitment: Using the New Science of Talent Analytics to Get Your Hiring Right or schedule a demo to understand how HireVue’s Talent Experience Platform can help you achieve your recruiting goals.

About the authors

Dr. Kiki Leutner, Director of Assessment Innovation at HireVue

Kiki is Director of Assessment Innovation, leading the development of innovative, machine learning-based assessments that are fair and psychometrically valid. She’s also a professor at Goldsmiths, University of London and regularly publishes her academic work in peer-reviewed journals. She is an expert in computational psychometrics, personality theory, and behavioral analytics. Kiki was part of the game-based assessment start-up MindX that HireVue acquired in 2018.

Dr. Reece Akhtar, CEO of Deeper Signals

Dr. Reece Akhtar is a co-founder and CEO of Deeper Signals. He is an organizational psychologist and data scientist specializing in applied personality assessment and computational psychometrics. As a lecturer at NYU and researcher at UCL, he has published scientific articles on personality, talent management, leadership, entrepreneurship, and machine learning. Previously he led product innovation at RHR International and Hogan Assessments Systems.