The Power of Absence: Thinking with Archival Theory in Algorithmic Design

Abstract

This paper explores the value of archival theory as a means of grappling with bias in algorithmic design. Rather than seek to mitigate biases perpetuated by datasets and algorithmic systems, archival theory offers a reframing of bias itself. Drawing on a range of archival theory from the fields of history, literary and cultural studies, Black studies, and feminist STS, we propose absence-as power, presence, and productive-as a concept that might more securely anchor investigations into the causes of algorithmic bias, and that can prompt more capacious, creative, and joyful future work. This essay, in turn, can intervene into the technical as well as the social, historical, and political structures that serve as sources of bias.

arXiv comment16 pages, to be published in the 2024 ACM Conference on Designing Interactive Systems (DIS '24)

References in corpus (5)

• Model Cards for Model Reporting
• Lessons from Archives: Strategies for Collecting Sociocultural Data in Machine Learning
• Machine Learning Uncertainty as a Design Material: A Post-Phenomenological Inquiry
• Bias Against 93 Stigmatized Groups in Masked Language Models and Downstream Sentiment Classification Tasks
• Archives and AI: An Overview of Current Debates and Future Perspectives

Cited by in corpus (1)

• Ontologies in Design: How Imagining a Tree Reveals Possibilites and Assumptions in Large Language Models