This Recommendation System is Broken

Through randomization, This Recommendation System is Broken decenters the viewer and reimagines the role of norms in collections.

Imagine that you are gazing at Claude Monet’s The Gare Saint-Lazare: Arrival of a Train. A typical recommendation algorithm will suggest that you look at other Impressionist paintings: why not another work by Claude Monet, perhaps Red Boats, Argenteuil? It relies upon conventional, longstanding, consecrated associations: between artists, art movements, periods, schools, and subject matters. The present project turns this familiar matchmaking practice on its head, seeking to transport the viewer from center to periphery, from the gallery wall to deep in the storage closet, from the highly valued to the undervalued and rarely seen.

This recommendation system is, in a sense, broken, which is to say, inaccurate. It does not respond to personal choices; it doesn’t collect data; nor does it attempt to interpolate preferences. Instead, it surfaces otherwise invisible artifacts in the collection, filtering for objects either rarely accessed or supported by incomplete records. The project employs randomization techniques to disrupt norms, to de-center the viewer’s perspective, to generate alternative instances of visibility and reimagine peripheries and borders.

“Brokenness” is a no less relative concept than wholeness. But here it is featured rather than concealed. The term becomes a window into the instrumental ethics of algorithms, one that observes how information filtering systems shape culture by reinscribing what is always already preferred. Brokenness provides an antidote by which further clicks reveal ever less familiar artists, defined objects, and identifiable regions. (Giulia Taurino)

Curatorial A(i)gents presents a series of machine-learning-based experiments with museum collections and data developed by members and affiliates of metaLAB (at) Harvard, a creative research group working in the networked arts and humanities.