The Augmented Dataset: Artistic Appropriations of Gans and Their Bearings on Ethical Considerations of Artificial Intelligence

Generative Adversarial Networks (GANs) have received much recent attention as they have been employed to falsify information through various media channels and to visually mislead viewers in their interpretation of still images and video. Decried under the rubric of fake news, GANs are often held up as malefactors in the crusade against unethical AI, yet their applications are wide ranging and their potential has yet to be fully realized. This presentation investigates the use of GANs by artists as an alternative to this narrative and considers the role of dataset formation in the Artificial Intelligence artistic process. Since such a significant number of images is required for machine learning systems to function well, the need to augment a dataset is often encountered and how this is overcome plays a considerable role in the final visual form of the GANs-produced image.

Indeed, artistic approaches to the hurdle to create new digital images through a repository of so many existing ones offer insights on what constitutes ethical Artificial Intelligence practices. The examples considered include those by notable Artificial Intelligence artists along with a recent project on gastronomic algorithms undertaken by the author with the Chef Alain Passard. Together, these artworks and projects lead one to the question: How can an image that is created through its computational yet obscured connection to a plethora of images be measured at all?

Emily L. Spratt (Columbia University)

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