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Experiments in Machine Unlearning: On Algorithmic Curation and Museums Marginalia

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We walk into museums with the expectation that objects will be exposed, exhibited, explained. Conceptual itineraries, chronological paths, geolocalized tours are designed throughout museum buildings and exhibition spaces to guide us across collections and make sense of their narratives. Yet, artworks onview in museums often count for a small percentage of the overall archive, made of objects, curiosities, undervalued art that remain temporarily hidden. While the work of curators is needed to filter what would otherwise be received as a meaningless hoard of objects, questions arise on the biases of art histories constructed on top of centuries of colonialist thinking. Bearing in mind this inner limit, the curatorial work has become a complex process of decolonization in search of new discoveries. How can digital tools help assist this process of reframing knowledge? This contribution focuses on the importance and responsibility of the curatorial process with the help of digital and computational tools. As part of this reflection on digital media art as critical making and critical design, I will present an ongoing project developed for metaLAB (at) Harvard’s exhibition series Curatorial A(i)gents (This Recommendation System is Broken, 2020-2022) and two similar prototypes for algorithmic curation created as part of the Nesta S+T+Arts City of the Future residency (Museums Marginalia, 2021). All projects blend critical algorithm studies with computational art, by taking a research-creation approach to digitized collections from a variety of museums. Research-driven art is intended here as a methodology focused on questioning, answering, exploring. By means of creative coding, this project problematizes the duality margin-center in art histories, reimagines alternative processes for machine learning and unlearning and defines new ecologies of care in the museum space. This interactive essay will ultimately consist of a guided visual journey through the works of art found thanks to computational curation.

Ph.D. is a Postdoctoral Research Fellow at The Institute for Experiential AI (Northeastern University). Her research focuses on forms of content organization on online platforms and digital archives, cultural implications of algorithmic technologies, and applications of artificial intelligence in the arts, heritage and museums sectors. Since September 2019, she is a research affiliate of metaLAB (at) Harvard, where she contributes to the project “Curatorial A(i)gents”, exploring the intersection between AI and curatorial practices. Previous residencies and awards include MITACS Award, FIAT/IFTA Grant, S+T+Arts x Nesta Italia - City of the Future prize, Zú Atrium x Unreal Engine - Creative Showcase. Giulia holds a doctorate degree in Media Studies and Visual Arts from the University of Bologna and the University of Montreal. She has served as a Director of Research and Innovation at AI Impact Alliance, where she worked on grant writing, project design, implementation research for AI ethics and sustainable development goals.