On Heuristic Models, Assumptions, and Parameters
Samuel Judson, Joan Feigenbaum
TL;DR
The paper identifies a neglected family of technical artifacts—heuristic models, assumptions, and parameters (HMAPs)—that encode social and normative decisions and can disproportionately shape the governance of computation. Through concrete domains such as differential privacy and post-quantum cryptography, it shows how seemingly simple numerical choices (e.g., $\epsilon$ or $L_{k\ell},U_{k\ell}$) or unverifiable assumptions can drive profound social outcomes and regulatory challenges. It argues for explicit recognition and systematic explanation of HMAPs, proposing a SoKfP approach to improve communication between technologists, policymakers, and practitioners, and outlines six hazards that complicate cross-disciplinary scrutiny. The work highlights the need for better education, transparent parameterization, and collaborative evaluation to reduce governance frictions and enhance accountability in computing innovations.
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.