Paolo Frazzetto

      Ritratto di Paolo Frazzetto

Computer Science and Innovation for Societal Challenges, XXXVII series 

Grant sponsor
Amajor SRL SB

Alessandro Sperduti



Project description

In my Ph.D. project, I am researching and developing computer-based tools for Amajor, a small-sized consultancy company founded in 2017 that deals with Business Development and Human Resources. Amajor has a precise vision: lead entrepreneurs to rediscover their enthusiasm and passion needed to pursue their dreams through the shared values and the complete fulfillment of their employees and partners. By generating a virtuous cycle in which the employee can achieve his full potential, both as a professional and as a person, Amajor’s ambitious goal is to create a better world thanks to businesses where their members, and consequently the community they are in, are better off. In detail, to carry out its Management and HR consulting services, Amajor uses proprietary questionnaires whose answers are mapped onto personality traits or on characteristics of the working environment. The output of the questionnaires is then presented and discussed with the client company’s personnel by an Amajor specialist, both to guide the client to become aware of its unexpressed potentials and improve the quality of the questionnaires themselves. This process of refinement over time, although practical, limits the exploitation of latent information contained in customer feedback due to the need for an Amajor specialist intervention. Current Machine Learning techniques would allow for the automatic and massive collection of data from the customers and a consequent continuous adjustment of the value of the model's parameters. Besides this immediate application, it is also considered feasible to evolve the mathematical models towards Artificial Intelligence systems capable of better representing the complexity of evaluating people’s behavior with different cultures or working habits. In addition, the administration of questionnaires and the related collection of feedback can be supported by a cloud service that could be delivered remotely to client companies. This would enable the establishment of a federated learning mechanism for the constant improvement of questionnaire output, which must be following current privacy regulations. Alongside these goals, other research opportunities arise by exploiting Process Mining techniques to integrate information collected via the questionnaires and data related to business processes to objectively guide and assess Amajor services' impact. Lastly, the development of AI-based tools could support recruiters in analyzing multimedia documents (CVs and video interviews) collected during the application phases for new corporate positions or internal staff mobility. This challenging project requires an interdisciplinary approach, linking computer science, work and industrial psychology, organizational theory, and business management. In the end, these tools not only will improve the match between a job position and an employee's skills and attitudes, but also they will provide valuable directions to promote healthier and more productive working places.