The Power of Collective Intelligence
Recruiters have become enamored with artificial intelligence and use it in many ways, including communicating with candidates, writing job descriptions, recommending candidates for jobs, and assessing skills and capabilities.
A.I., when used effectively, improves the speed of recruitment and the quality of the candidates. But there is one major caveat. A.I. alone is not capable of providing the best candidates. Its intelligence is just as limited as ours, only in different ways.
Geoff Mulgan, author of The Big Mind: how collective intelligence can change our world, explains what intelligence means.
If we break intelligence down into its parts—observation, analysis, memory, creativity, judgment, and wisdom – we can see that artificial intelligence is excellent at finding patterns in data, analysis, and memory. But it is deficient in judgment, wisdom, creativity, and empathy.
These are human traits, and when we combine the knowledge, wisdom, and judgment of multiple people, combined with artificial intelligence, we gain the most in terms of understanding and in making valuable predictions.
Multiple Inputs are Often Better than Single Inputs
Making decisions about people is extremely difficult. People are complex, often act in unpredictable ways, and perform differently depending on the context of the work and the nature of their workgroup and leader. One individual observing or working with another has a limited perspective on their behavior, personality, and work quality. This is why it is so important to gather inputs about people from as many sources as possible. The accuracy of our appraisal of someone improves as we add more observations, get a picture of the person in different circumstances, and solicit inputs from diverse people who have interacted with them and are free of any social pressure.
Algorithms can assess factual data such as skills, experience, and analytical ability, but they are poor at understanding whether a person has integrity, exercises good judgment, or shows creativity. They learn from existing data, and because humans are biased, the data is also often biased.
Collective Intelligence Reduces Bias
An individual can be prejudiced against someone or biased about their performance, but it is less likely that a group of people would have the same biases. Recruiters can use the Internet to contact past and current colleagues, bosses, and friends and ask for their opinions and observations. These multiple inputs enrich and expand an individual’s limited data that an algorithm can find in a social media profile. Soliciting diverse viewpoints from many individuals lessens the impact of any one person’s biases or prejudices as each other biases will cancel each other.
As Overview of Candidate Assessment
When we talk about assessing a candidate for a position, it is crucial to have as broad and accurate a picture of their skills, attitudes, experience, and motivation as possible.
Interviews are the most common form of assessment but have many limitations. The academic research is consistent. The typical, unstructured interview, where recruiters ask whatever questions they feel are relevant and where the same questions may not be asked of every candidate, are unreliable or valid. A structured interview, where questions are focused on one or two areas and are delivered consistently to every candidate by each recruiter, can reach high validities.
No matter how they are done. Usually, only a few people participate and may not look for the same characteristics. The interviewer may be prejudiced. And no interviewer has seen that candidate at work or has any deep knowledge of their personality.
A.I.-powered screening and assessment tools are gaining in popularity but can only provide a partial picture based on easy-to-define criteria and by comparing current applicants to past successful hires
A more robust and accurate way of assessing a candidate would be to combine the results of any A.I.-powered results with interviews and online assessments with feedback and insight from recruiters and colleagues. By doing this, we can be assured that we have as accurate a portrayal of the person’s capabilities as possible.
Using collective human intelligence allows recruiters and team leaders to make better hiring decisions. And by making better decisions about who gets hired, team performance and retention improve.
Different Assessments for Different Types of Candidates
The algorithms used in most available products are inadequate to assess every candidate type. Most accurate algorithms are designed to look for specific skills, experiences, education, and other factors that combine to provide a probability that a generic candidate would be a good hire. Even though the algorithm’s creators may have tried to introduce “fuzzy” logic to try and give some flexibility to the output, the results are much different from those made by a human.
Algorithms can determine a candidate’s personality by scanning their social media network characteristics and behavior patterns and matching them to other previously analyzed profiles. If a candidate has no profile, the tools are useless. Likewise, if the profile is limited, the assessment will lack credibility.
Assessing Technical Experts
These are the current “darlings” of the recruitment and work world. Finding quality experts is the focus of most recruiters and the area where they spend the most time. This category includes all technical, engineering, software, and hardware positions and positions where deep expertise is valued and where advanced degrees are often required, including in functions such as legal, human resources, and finance. These positions require people who follow the rules and apply judgment, often augmented by mathematics or statistics, to make decisions. Using best practices and acting predictably are essential. Everything is aimed at not making mistakes.
Personability profiling and algorithm-driven assessments can provide insight into skills and experience, but additional assessments, including the collective intelligence of colleagues, expand the data, allows for more certainty in the final decision, and provides an indication of the person’s judgment and creativity.
Assessing Soft-Skill Professionals
These positions require skills involving working with others, influencing, challenging, coordinating, and connecting with a global team. Soft-skill professionals work across cultures, need to build strong relationships, and need to have creative decision-making capabilities to deal with unknown and unknowable challenges.
Personality profiling and skills assessment provide only a bit of insight into how well these professionals will perform. Algorithms find it hard to judge a person’s comfort in teams, whether they are willing to share ideas or if they make good decisions. Soliciting the collective observations of colleagues and teammates is invaluable in assessing the person’s empathy, creativity, and ability to make sound decisions.
Summary
Collective intelligence is essential to augmenting the analytical power of artificial intelligence. We should always choose to use both A.I. and collective intelligence. Either acting alone is less accurate and less predictive than when combined. It is a mistake to believe that A.I. can replace human judgment at this stage of development. We do not have enough diverse data to be confident of the results. At best, the results of an A.I. analysis can provide some guidance and offer areas where a recruiter needs to probe more deeply or learn more about the situation involved. Humans can look at the context of a situation, assess the individual in that context, and apply the filters of experience to their recommendation.
Recruiters need to embrace the power of A.I. but not be fooled into thinking it can replace them – at least not yet.
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