Latent Space: From Datasets to Digital Heritage
When:
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Venue:
Birkbeck 43 Gordon Square
Latent Space: From Datasets to Digital Heritage
with Aarati Akkapeddi, Ananda Rutherford, and Anna Ridler
Issues of incomplete data, acquisition transparency, and problematic taxonomies are topics that arguably span both Archival Ethics and AI Ethics. The bias of major Machine Learning datasets like Imagenet is something that has been widely criticised, leading to debates over collection methodologies and consent. At the same time, in the past decade, many museums have undertaken mass digitisation efforts, producing large amounts of digital data. This event will look at how both digital collections and digital datasets bring up similar ethical themes such as:
- The traces of often troublesome histories of taxonomy in the ways we categorise and classify digital data today
- The subtle and not-so-subtle ways bias is embedded in image descriptions
- Complexities of automating dataset/catalogue auditing (for example moving beyond keyword searches for finding and amending euphemistic, racist or sexist catalogue descriptions or data.)
- Terms & Agreements: rethinking open access, collecting and consent from source-communities
- The (human) labour involved in creating and maintaining digital data.
- The ethical implications of using digital heritage as training data for Machine Learning.
This event will include two presentations, a workshop, and an open discussion (lunch will be provided):
Artist Aarati Akkapeddi will present their current creative exploration of the V&A's collection and speak about the connections they find between datasets, collections and archives in their own practice.
Researcher Ananda Rutherford will discuss her work on Transforming Collections: Reimagining Art, Nation and Heritage at the Decolonising Arts Institute, UAL.
Artist Anna Ridler will lead a hands-on workshop looking at several canonical computer vision datasets. This workshop will try to shed light on what has gone into training recent large language models (obscured and opaque). Participants will start to build their own datasets, concentrating on words that are difficult to define, images that are hard to classify and languages that no longer exist.
The event is sponsored by the Vasari Research Centre for Art and Technology and the Experimental Humanities Collaborative Network.
Contact name:
Joel Mckim
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Aarati Akkapeddi
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Aarati Akkapeddi is a first-generation, Telugu-American, interdisciplinary artist, educator, and programmer interested in the poetics and politics of datasets. Their work touches on themes of intergenerational memory, loss, and diaspora. Akkapeddi works with both personal and institutional archives to explore how identities and histories are shaped by different methods of collecting, preserving, and presenting data. They combine code, machine learning and analog techniques (photography, printmaking, & embossing), and often use family photographs and archival images as a source material, creating performative rituals of information extraction.
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Ananda Rutherford
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Ananda Rutherford is a museum collections manager and researcher. In recent years her research and professional practice has focussed on the documentation of museum objects, how information is presented, captured and accessed. She is interested in the history and role of museum documentation and the digitisation of museum collections, but increasingly her focus is on the ethics of cataloguing practice in relation to structural racism and colonialist thinking.
Her background is in art history, material culture and the history of the decorative arts and she has been working with UK museum collections such as: Ashmolean, V&A, the Museum of the Home, Sir John Soane’s Museum, Crafts Council.
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Anna Ridler
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Anna Ridler is an artist and researcher who works with systems of knowledge and how technologies are created in order to better understand the world. She is particularly interested in ideas around the natural world. Her process often involves working with collections of information or data, particularly datasets, to create new and unusual narratives.