The possibilities, limitations, and responsibilities that come with instructing both humans and computers how to see stand at the core of A Well-trained Eye. The exhibition considers the potential to classify and surveil that we have coded into our AI technologies, looking closely at the biases that underlie data collection and analysis. Encompassing different media—drawing, photography, video, and installation—the artworks on view examine the reciprocal relationship between human and computer vision; they interrogate how technical systems increasingly mediate our relationship with ourselves, the environment, and to one another, fundamentally shaping the way we see.

What does our training of computer vision models say about the way we perceive the world and about the future we imagine? How are these developments impacting the expectations and interactions we have with our bodies and those of others? By establishing a parallel between training algorithms and the human process of learning to see, A Well-trained Eye reflects on how the developers of artificial intelligence have ingrained their worldviews in their creations, including certain principles, associations and aesthetic preferences. At a pressing time when machine learning tools are reconfiguring not just the economy, but also how we value academic learning, labor, privacy, and the quality of our interpersonal relationships, this exhibition thinks critically about  the categories and value systems currently driving these new technologies, inviting us to unlearn some of our own ways of seeing.

Curator: Maria Emilia Fernandez