# Problem Formulation and Fairness

## TL;DR

This paper explores how the process of formulating data science problems involves complex negotiations and often lacks explicit normative considerations, impacting fairness and ethical outcomes.

## Contribution

It provides an ethnographic analysis of problem formulation in data science, highlighting its negotiation process and implications for fairness and normative assessment.

## Key findings

- Problem formulation is negotiated and elastic.
- Explicit normative considerations are rarely incorporated.
- Formulation choices significantly influence fairness outcomes.

## Abstract

Formulating data science problems is an uncertain and difficult process. It requires various forms of discretionary work to translate high-level objectives or strategic goals into tractable problems, necessitating, among other things, the identification of appropriate target variables and proxies. While these choices are rarely self-evident, normative assessments of data science projects often take them for granted, even though different translations can raise profoundly different ethical concerns. Whether we consider a data science project fair often has as much to do with the formulation of the problem as any property of the resulting model. Building on six months of ethnographic fieldwork with a corporate data science team---and channeling ideas from sociology and history of science, critical data studies, and early writing on knowledge discovery in databases---we describe the complex set of actors and activities involved in problem formulation. Our research demonstrates that the specification and operationalization of the problem are always negotiated and elastic, and rarely worked out with explicit normative considerations in mind. In so doing, we show that careful accounts of everyday data science work can help us better understand how and why data science problems are posed in certain ways---and why specific formulations prevail in practice, even in the face of what might seem like normatively preferable alternatives. We conclude by discussing the implications of our findings, arguing that effective normative interventions will require attending to the practical work of problem formulation.

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Source: https://tomesphere.com/paper/1901.02547