[ISEA2022] Poster: Janna Ahrndt — Generating Condolences: Coding Grief During Covid-19

Poster Statement 

June 13-15, CCCB Vestíbul Theatre 

Keywords: Machine Learning, Mourning, Greif, Covid-19, Generative Art

The text on each card in generated using a predictive algorithm that uses a dataset of Hallmark Cards and Political Speeches about Covid-19 relief packages. The images are derived using a machine learning process called a GAN (Generative Adversarial Network) trained on a dataset of mourning sewing samplers from the 1700’s and 1800’s.

There is a long-standing expectation for feminine sentimentality in Western culture. Women have been the family historians and curators, saving objects and images to later be passed down as the family archive. This expectation extends into cultural practices surrounding mourning and death. Often in times of mourning women are the primary archivists and creators of memorial and sentimental objects for the family after the death of a loved one. In this paper I try to reconcile the traditional role of mourning ritual – language and iconography with generative computing art making in the time of Covid-19. https://deepestcondolence.com

  • Janna Ahrndt received her MFA in Electronic and Time-Based Art from Purdue University, USA. She is part of a wave of new media artists rejecting the notion that craft and technology are directly opposed. Her work explores how deconstructing everyday technologies, or even making them for yourself can be used to question larger oppressive systems and create a space for participatory political action. Her activist and social art practice blur the lines between the materiality of craft and the digital realm of new media technologies to create socio-political interventions.