Intersectionality Goes Analytical: Taming Combinatorial Explosion Through Type Abstraction

TL;DR

This paper introduces a formal model using type abstraction to manage the complexity of intersectional analysis in HCI, reducing costs and enabling reuse of prior work across populations.

Contribution

It presents a formal (de)compositional model for intersectional analysis, proving its correctness and demonstrating its practical utility in HCI design.

Findings

Reduces empirical effort in intersectional HCI work.

Enables reuse of analytical methods across different populations.

Validated through four practical design case studies.

Abstract

HCI researchers' and practitioners' awareness of intersectionality has been expanding, producing knowledge, recommendations, and prototypes for supporting intersectional populations. However, doing intersectional HCI work is uniquely expensive: it leads to a combinatorial explosion of empirical work (expense 1), and little of the work on one intersectional population can be leveraged to serve another (expense 2). In this paper, we explain how representations employed by certain analytical design methods correspond to type abstractions, and use that correspondence to identify a (de)compositional model in which a population's diverse identity properties can be joined and split. We formally prove the model's correctness, and show how it enables HCI designers to harness existing analytical HCI methods for use on new intersectional populations of interest. We illustrate through four design use-cases, how the model can reduce the amount of expense 1 and enable designers to leverage prior work to new intersectional populations, addressing expense 2.