AI-driven Team Matching Using a Novel Personality Profile, Affinity Score and Fairness Measures

Abstract: Research into the matching of employees in teams is crucial, as the success of a team depends heavily on the right composition. Individual skills, personalities and working styles can have a significant impact on team dynamics, collaboration and productivity. Targeted matching strategies can reduce conflicts, strengthen synergies and increase employee satisfaction, which ultimately promotes a company's innovative strength and competitiveness. Our research aims at the development of an e-tool that systemically matches people into existing teams based on their personality structure, professional role, and an algorithm for affinity mapping processes. We developed a novel methodology and decision support for recruitment being based on AI-based matching of personality profiles. We firstly apply a novel psychometric profile of personality structure being inspired by Häusl (2019), focusing on limbic-aligned dimensions: security and socialization, dominance and autonomy, stimulant and curiosity, challenge and risk, empathy and team, discipline and control. Furthermore, a quantitative collaboration assessment integrates features of communication, operation, relationship, and emotion, from the analysis of the interaction of personalities, defining unconscious and controlling patterns of thought and behavior and their impact on collaboration. The core components of our self-learning evaluation model include an algorithm that records and processes the relationships between personality and role system in the form of individual data and, derived from this, creates recommendations in the form of affinity matrices for the successful composition of teams. We define AI-driven assessment functions to determine the entries of an affinity matrix to be based on personality matching between a recruited and a given team. In a first step we apply a machine learning with explainable decision making that scores a cooperation potential based on the pairing of two personality profiles between 1 and 10 (maximum). We then seek for an optimal pairing based on a given team, starting with dyadic relation with team leaders, given a space of possible recruited that is developed with real and synthetic data. Finally, the affinity matrix is determined based on the recruited and compared to the optimally sampled individual. We present first results of the novel profile- and AI-based methodology. We recruited more than 90 personality profiles as well as 60 pairings, determined the cooperation score and applied expectation-maximization method (Dempster et al., 1977) to generate synthetic data to find optimally recruited. Fairness-based measures were applied in order to monitor potential bias in the distribution in the context of gender, such as, sex, age, or ethnic origin. Applying an ensemble of bagged trees (Zoghni, 2020) to estimate collaboration performance achieved a mean absolute error of 0.93 score points, using cross-correlation for training and test set data separation. Entrepreneurs and managing directors are facing complex challenges in human resources (HR) management; the pandemic has changed working morale and working models. Recruitment is time-consuming and becoming increasingly complex. The future target group are self-employed people and small and medium-sized enterprises. The application would range from individual tests or packages for the self-employed and small companies to a white-label service for large companies.

Keywords: Human Resources, Personality, Affinity Analysis, Machine Learning, Explainable AI.

DOI: 10.54941/ahfe1006099

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