On Heuristic Models, Assumptions, and Parameters

TL;DR

This paper highlights the importance of understanding and scrutinizing heuristic models, assumptions, and parameters in computing, emphasizing their impact on societal effects and the need for careful analysis in interdisciplinary research.

Contribution

It identifies and discusses three classes of technical artifacts—heuristic models, assumptions, and parameters—that are often overlooked but crucial for understanding computing's social impact.

Findings

Heuristic models, assumptions, and parameters can obscure the understanding of computing's social effects.

These artifacts are used to mask incomplete theoretical foundations and shift normative responsibility.

Careful scrutiny of these objects is essential for responsible sociotechnical analysis.

Abstract

Insightful interdisciplinary collaboration is essential to the principled governance of technology. When such efforts address the interaction between computation and society, they often focus on modeling, the process by which computer scientists formally define problems in order to enable algorithmic solutions. But modeling is a multifaceted and inherently imperfect process. Especially in interdisciplinary work, it often receives uneven scrutiny because of the practical challenges of communicating complex technical details to non-experts. We argue that there is an underappreciated if loose family of obscure and opaque technical caveats, choices, and qualifiers that the social effects of computing can depend just as much on as far more heavily scrutinized modeling choices. These artifacts are often used by researchers to paper over the incomplete theoretical foundations of computing or to burden shift responsibility for the impact of normative design decisions. Further, their nuanced technical nature often complicates thorough sociotechnical scrutiny of the discretionary decisions made to manage them. We describe three specific classes of such objects: heuristic models, assumptions, and parameters. We raise six reasons these objects may be hazardous to comprehensive analysis of computing and argue they deserve deliberate consideration as researchers explain scientific work.