"Humans also outperform machines when it comes to tasks that require creativity and a high degree of complexity that is not routine. As soon as you require flexibility, the human does better," said Damian, lead author of the study published in the European Journal of Personality.
Researchers used a dataset of 346,660 people, looking at personality traits and vocational interests in adolescence, along with intelligence and socioeconomic status.
The study is the first to look at how a variety of personality and background factors predict whether a person will select jobs that are more (or less) likely to be automated in the future.
"We found that regardless of social background, people with higher levels of intelligence, higher levels of maturity and extroversion, higher interests in arts and sciences tended to select (or be selected) into less computerisable jobs 11 and 50 years later," researchers said.
The findings suggest traditional education may not be fully equipped to address upcoming changes in the labour market, Damian said.
"Perhaps we should consider training personality characteristics that will help prepare people for future jobs," she said.
The researchers found that every 15-point increase in IQ predicted a seven per cent drop in the probability of one's job being computerised, the equivalent of saving 10.19 million people from losing their future careers to computerisation if it were extrapolated across the entire US population.
While IQ is not easily changed, a solution could be to find effective interventions to increase some personality traits - doing well in social interactions, for example, or being industrious - or interest in activities related to the arts and sciences, Damian said.
Machine learning and big data will allow the number of tasks that machines can perform better than humans to increase so rapidly that merely increasing educational levels would not be enough to keep up with job automation, she said.
Click here for more Jobs News
(This story has not been edited by NDTV staff and is auto-generated from a syndicated feed.)