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Intelligent Digital Humans for Bias-Free Recruitment Interviews: A Diversity & Inclusion Training Program

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Creative Approaches to Technology-Enhanced Learning for the Workplace and Higher Education (TLIC 2023)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 767))

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Abstract

This article is about an intelligent digital human model enhanced by artificial intelligence, designed to meet the requirements from a multinational company in need of training for their human resources personnel on bias-free recruitment interviews. We have been creating a new generation of avatars with social intelligence, who are capable not only of presenting a wide variety of topics in a dynamic and engaging manner but also of interacting with the audience and communicating emotions and moods. We have been customizing avatars for role plays, building them as real interlocutors who facilitate training in how to handle difficult conversations by including aspects such as non-verbal communication, different communication styles, and diversity and inclusion. Practicing conversations with avatars accelerates learning from experience without the risks associated with learning in the field. At the end of each interview, timely feedback is provided so learners can determine how to improve their performance. These digital humans are able to perform like realistic human beings, challenging the interviewer both at a verbal and para-verbal level, as well on the cognitive and the emotional levels — making it easy for the interviewer to get trapped into biases and false assumptions. The key message is this: diversity and inclusion best practices are, first of all, about mindset.

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Correspondence to Fernando Salvetti .

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Salvetti, F., Bertagni, B., Contardo, I. (2023). Intelligent Digital Humans for Bias-Free Recruitment Interviews: A Diversity & Inclusion Training Program. In: Guralnick, D., Auer, M.E., Poce, A. (eds) Creative Approaches to Technology-Enhanced Learning for the Workplace and Higher Education. TLIC 2023. Lecture Notes in Networks and Systems, vol 767. Springer, Cham. https://doi.org/10.1007/978-3-031-41637-8_36

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