Machines are going to have a say in who we select as leaders, according to an Otago University-led study.
Organisations already use machine learning (a subset of artificial intelligence) to aid in the hiring process, but they also hold masses of other untapped data, particularly related to leaders.
Lead author of the study, Dr Brian Spisak, claims “basically, where there’s data, there’s going to be some sort of machine learning algorithm exploring patterns.
“It will shape how we select leaders (and followers), build teams, make decisions at a strategic level, adjust patterns of behaviour in real-time, dictate marketing plans, and adjust how we distribute budgets and make investments.”
The study, published in The Leadership Quarterly, used self-reported personality data and performance evaluations of 973 managers from a range of areas. The researchers used machine learning to investigate the theory of different personality traits being linked to successful leadership.
“We realised it’s only a matter of time until the mountain of leadership personality data is used to predict leader effectiveness. It’s increasingly important to better understand what machine learning can add as well as what it can’t.”
Brian then asks “how much should we rely on the machine?” in light of how people might feel about a machine judging their potential to become CEO.
The study found that personality predicted performance and, importantly, that the context of the situation significantly improved the ability to calculate leader performance.
“This predictive boost from context implies that no matter how charismatic, extroverted, or generally amazing a leader is, if the situation is not conducive, then leaders will struggle.
“So leaders may want to choose their situations wisely. Just because someone offers you the opportunity to be prime minister of the UK, for instance, should you take it during these Brexit times (especially if you’re worried about longevity)? I don’t know much about Theresa May as a leader, but the cards were definitely stacked against her,” says Brian.
While machine learning is influential, Dr Spisak argues that caution is necessary.
“Machine learning is ethically void. It will find patterns, but that does not mean we should act on them. Inadvertently using the wrong fuel in the machine learning engine has the potential to damage both the engine and the operator,” says Brian.