Borderland encounters -evolving professional identities between human and machine learning processes (original) (raw)

Nowadays, the ability to take initiative and to assert oneself, but also to care for others and show empathy, are considered as essential requirements in many professional settings. Those abilities have become part of the professional identity of many team leaders or department heads, as well as of collaborators in organizations with less hierarchical structures. Like other specialized skills, they are object of training and upskilling measures. As the approach to professional training has been changing significantly, more and more offers for experience-based, interactive and playful forms of training have been created. Recent years have seen an increase in the use of interactive training applications based on virtual reality (VR) and artificial intelligence (AI) technologies for developing social skills, such as public speaking, interpersonal communication abilities and so on. Considering that social and interactive skills are seen as distinctive human traits, the AI-powered digital space may constitute a borderland in which human and machine learning processes intertwine. Still, whereas machine learning processes function exclusively on the basis of pattern recognition, human learning and interaction is characterized by an openness that includes breaches, failures, external appraisal, critical reflection, and other forms of transformation. Using the case of an interactive VR environment for the training of social skills, we outline a new theoretical approach for mapping the borderland between human and machine learning processes in the field of social skills training. We discuss the case within the framework of Gabriel Tarde's concepts of invention, imitation and desire.