Do Abstractions Have Politics? Toward a More Critical Algorithm Analysis

TL;DR

This paper advocates for a critical, justice-centered approach to algorithm analysis in computer science education, emphasizing how data structures and algorithms embody political values and social implications.

Contribution

It introduces affordance analysis as a novel method to critically examine the political and social values embedded in computational abstractions.

Findings

Affordance analysis reveals political values in data structures and algorithms.

Case studies show how design choices impact social benefits and harms.

The approach challenges the social determinism of technology.

Abstract

The expansion of computer science (CS) education in K--12 and higher-education in the United States has prompted deeper engagement with equity that moves beyond inclusion toward a more critical CS education. Rather than frame computing as a value-neutral tool, a justice-centered approach to equitable CS education draws on critical pedagogy to ensure the rightful presence of political struggles by emphasizing the development of not only knowledge and skills but also CS disciplinary identities. While recent efforts have integrated ethics into several areas of the undergraduate CS curriculum, critical approaches for teaching data structures and algorithms in particular are undertheorized. Basic Data Structures remains focused on runtime-centered algorithm analysis. We argue for affordance analysis, a more critical algorithm analysis based on an affordance account of value embedding. Drawing on critical methods from science and technology studies, philosophy of technology, and human-computer interaction, affordance analysis examines how the design of computational abstractions such as data structures and algorithms embody affordances, which in turn embody values with political consequences. We illustrate 5 case studies of how affordance analysis refutes social determination of technology, foregrounds the limitations of data abstractions, and implicates the design of algorithms in disproportionately distributing benefits and harms to particular social identities within the matrix of domination.