Can Machines Learn Creativity Needs? An Approach Based on Matrix Completion

Abstract

Technological progress has been recently associated with a crowding-out of cognitive-skill intensive jobs in favour of jobs requiring soft skills, such as ones related to social intelligence, flexibility and creativity. The nature of soft skills makes them hardly replaceable by machine work and among subsets of soft skills, creativity is one of the hardest to define and codify. Therefore, creativity-intensive occupations have been shielded from automation. Given this framework, our study contributes to a nascent field on interdisciplinary research to predict the impact of artificial intelligence on work activities and future jobs using machine learning. In our work, we focus on creativity, starting from its possible definitions, then we get significant insights on creativity patterns and dynamics in the Italian labour market, using a machine learning approach. We make use of the INAPP-ISTAT Survey on Occupations (ICP), where we identify 25 skills associated with creativity. Then, we apply matrix completion—a machine learning technique which is often used by recommender systems—to predict the average importance levels of various creative skills for each profession, showing its excellent prediction capability for the specific problem. We also find that matrix completion typically underestimates the average importance levels of soft skills associated with creativity, especially in the case of professions belonging to the major group of legislators, senior officials and managers, as well as intellectual professionals. Conversely, overestimates are typically obtained for other professions, which may be associated with a higher risk of being automated.

The emergence of social soft skill needs in the post COVID-19 era

“…To conclude, our research does not exhaust the possible analyses that can be performed based on the ICP dataset, possibly focusing on other variables of interest. For instance, in Gnecco, Landi, and Riccaboni (2022), we analyzed the average importance levels of soft skills related to creativity. Other possible future analyses could be focused, e.g.…”

“…Finally, to derive the implications of our analysis of social soft skills endowments across sectors and workers' age groups, we combine the results obtained by MC with the Microdata for Research (MFR) on the Continuous Detection of Labor Force (RCFL) 3 provided by the Italian National Institute of Statistics (ISTAT)-which gives us the economic sector and activity workers are associated with, and their age group-and with ISTAT data on the Italian working population. It is worth noticing that no combination of the ICP dataset with the MFR RCFL dataset and with ISTAT data on the Italian population was performed in the previous work Gnecco, Landi, and Riccaboni (2022). We show that among selected social soft skills, cooperating, managing working groups, coordination with others, teamworking, and teaching are among the most negatively impacted in the simulated post-COVID-19 scenarios (i.e.…”

“…A soft skill can be broadly defined as knowledge in the human mind that is extremely personal, hard to formalize, and quite difficult to acquire naturally, as its development is based on personal experience (Lee 2019). One example of a soft skill (or, more precisely, a set of soft skills) is creativity, which is reviewed in Gnecco, Landi, and Riccaboni (2022). Indeed, soft skills are grounded in specific actions and experiences, which include emotionality, idealism, and values (Cirillo et al 2021).…”

“…generalization capability in this specific application. It is worth mentioning that the baseline scenario was also studied in Gnecco, Landi, and Riccaboni (2022), focusing, however, on a different subset of soft skills to evaluate the MC performance. In that work, possibly due to the absence of any perturbation on the original occupation matrix, the MC application to that scenario produced even smaller (empirical) means and standard deviations for the RMSE (on the test set) per profession achieved by the MC prediction.…”

“…The same MC approach was applied with success (i.e. showing excellent prediction accuracy) in Gnecco, Landi, and Riccaboni (2022) to analyze the average importance levels of soft skills for creativity. In that work, however, no matrix perturbation induced by a simulated post-COVID-19 scenario was considered (i.e.…”