Preparing humane ML experts for a better future. Experiments with design and engineering students

Recognizing the rising demand for well-trained professionals in the responsible AI (RAI) landscape, the study explores which skills might characterize humane ML experts. A literature review outlines human centricity, ML-savvy, and value sensitivity as pivotal qualities for responsible practices, materializing an overarching multidisciplinary approach to the design of meaningful ML-infused solutions. For a preliminary and qualitative investigation, four experimental workshops were conducted in different European universities, targeting design and computer engineering students across different educational levels. They were intended to (i) translate the presented skills into educational experiences; (Q2) assess the effectiveness of the experimentations to foster these competences; and (Q3) evaluate their suitability and meaningfulness. Adapting the theoretical assumptions to the target audiences' backgrounds, positive results emerged. Both design and engineering students exhibited receptiveness and appreciation for the contents, methods, and tools presented in the workshops, emphasizing the transversal and essential nature of the proposed skills in diverse educational contexts. Despite the limits of the experimentations, the research argues that the depicted skills might orient designerly and technical ML experts toward meaningful outcomes, especially if they build effective collaborations leveraging their complementary strengths. Hopefully the contribution offers insights to advance the discourse about future professional figures in RAI.