Can We Really Assess Talent With A.I.?
Can We Really Assess Talent With A.I.?
For the past few years two different perspectives on assessment have come to the forefront. One is that assessment can be taken over by automated tools using artificial intelligence and machine learning. The other is that only humans can really assess humans. The reality is that by using both artificial intelligence and human knowledge and experience we can get a more accurate assessment than we can with either used alone.
Together these are called collective intelligence.
Geoff Mulgan, author of The big Mind: how collective intelligence can change our world, provides some of the building blocks for understanding what we mean by collective intelligence. If we break intelligence down into component parts—observation, analysis, memory, creativity, judgement and wisdom – we can see that computers are excellent at observations, analysis and memory but are deficient in judgement, wisdom, creativity and empathy. These are human traits and when we combine the knowledge, wisdom and judgement of multiple people, combined with artificial intelligence, we gain the most in terms of understanding and in making good predictions about people.
Years of research have shown that groups generally make more accurate or more valid decisions than do individuals. The power of teams is well documented, and most organizations today rely on teams of employees working together to solve problems and achieve goals.
Collective Intelligence Reduces Bias
The Internet has made it possible to reach out to large numbers of people who may be scattered around the globe. Past colleagues, bosses, and friends can be asked for opinions and observations which enrich and expand the limited data one individual may have or that can be found on a social media profile. Soliciting diverse viewpoints from many individuals can lessen the impact of any one person’s biases or prejudices.
When we talk about assessing a candidate for a position, it is important to have as broad and accurate picture of their skills, attitudes, experience, and motivation as possible.
Tools that allow for collecting multiple perspectives have an advantage over purely automated tools. Using collective human intelligence, recruiters allow team leaders to make better hiring decisions. Simply by making better decisions about who gets hired, team performance improves as well as retention.
Different Assessment for Different Types of Candidates
One algorithm is most likely not adequate to assess every type of candidate. The most accurate algorithms are designed to look for specific skills, experiences, education and other factors that combine to provide a probability that a candidate would be a good hire.
Learning algorithms can determine a candidate’s personality by scanning their social media networks characteristics and patterns of behavior that match other previous analyzed profiles. If a candidate has a no profile the tools are useless. Likewise, if the profile is limited the assessment will lack credibility.
Assessing Technical Experts
Technical experts 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 rules, but also apply judgement, often augmented by mathematics or statistics, to make decisions. Using best practices and acting in a predictable manner are essential. Everything is aimed at not making mistakes.
Personability profiling and algorithm-driven assessments can provide insight into specific skills and experience but additional assessments, including the collective intelligence of colleagues, expands the data, allows for more certainty in the final decision, and provides an indication of the person’s judgement and creativity. A.I. tools lack the abulity to determine if an exgineer, for example, is creative or just soemone who follows past methods.
Assessing Soft-Skill Professionals
These positions require skills that involve working with other people, 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 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.
I am huge advocate of using automated tools wherever we can. In most cases, these tools can do a preliminary screen effectively. In many routine hiring situations, they can completely and accurately assess an individuals liklihood of success. But for complex positions involving creativity, judgement, and empathy they are still lacking. Perhaps as A.I. develops it will be able to better assess sthese but as of today these are areas where humans make better judgements.
Collective intelligence is essential to augmenting the analytical power of artificial intelligence. Either A.I. or collective intelligence, acting alone, is less accurate and less predictive than when combined. It is a mistake to believe that A.I. can completely replace human judgement at this stage of development. Humans have the capability to look at the context of a situation, assess the individual in that context, and apply the filters of experience to any recommendation.
I would appreciate any comments or thoughts you have on this topic.
Collective intelligence describes how well teams solve complex problems. It doesn’t depend on individual smarts but on good collaboration: social perceptiveness and evenness of communication.
In the age of artificial intelligence, do our AI systems need empathy? If so, what are some use cases where empathy can be most helpful in AI Systems? Humans are social creatures. We thrive on empathy.
Assess Non Technical Skills With AI — Guide For Recruiters (Warning: written by a vendor, but interesting.)
Recruit for essential non technical skills at scale using AI. Guide for recruiting and HR includes list of non technical skills and example tests.
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