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

arXiv: https://arxiv.org/abs/2405.05420 · HTML: https://mlsanity.com/paper/2405.05420/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 May 2024.

## Built on by (most cited first, 5 of 5)

- [Provocations from the Humanities for Generative AI Research](https://mlsanity.com/paper/2502.19190/) (2025)
- [Ontologies in Design: How Imagining a Tree Reveals Possibilites and Assumptions in Large Language Models](https://mlsanity.com/paper/2504.03029/) (2025)
- [Social Science Is Necessary for Operationalizing Socially Responsible Foundation Models](https://mlsanity.com/paper/2412.16355/) (2024)
- [CREDAL: Close Reading of Data Models](https://mlsanity.com/paper/2502.07943/) (2025)
- [Reciprocal Collaboration: how lessons from convergence in GLAMs can enhance interdisciplinary AI research](https://mlsanity.com/paper/2609.26023/) (2026)

## Abstract (arXiv)

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.