Scurto, Caramiaux, Bevilacqua, 2019—2021
research-action-creation method
A diffractive method for machine learning prototyping, to resist normative framings of AI. I propose to craft machine learning, using it as creative material in art and design projects, involving diverse bodies and beings. Reflecting on my experience with this practice in interdisciplinary contexts, I identify five socio-technical conditions that enables to interfere design and engineering in machine learning prototyping: situational whole, small data, shallow model, learnable algorithm, and somaesthetic behaviour. I suggest that these interferences are vital in reshaping practices and discourses in AI.