Behavior Matters: An Alternative Perspective on Promoting Responsible Data Science

TL;DR

This paper proposes a new approach to responsible data science by applying behavior change theories to influence practitioners' ethical practices and reduce social harm.

Contribution

It introduces a novel perspective integrating behavior change theories with data science workflows to promote ethical responsibility beyond technical fixes.

Findings

Behavior change theories can guide interventions in machine learning and data analysis.

Integrating psychology and ethics offers new pathways for responsible data science.

Call to action for community engagement in behavior-focused data science research.

Abstract

Data science pipelines inform and influence many daily decisions, from what we buy to who we work for and even where we live. When designed incorrectly, these pipelines can easily propagate social inequity and harm. Traditional solutions are technical in nature; e.g., mitigating biased algorithms. In this vision paper, we introduce a novel lens for promoting responsible data science using theories of behavior change that emphasize not only technical solutions but also the behavioral responsibility of practitioners. By integrating behavior change theories from cognitive psychology with data science workflow knowledge and ethics guidelines, we present a new perspective on responsible data science. We present example data science interventions in machine learning and visual data analysis, contextualized in behavior change theories that could be implemented to interrupt and redirect potentially suboptimal or negligent practices while reinforcing ethically conscious behaviors. We conclude with a call to action to our community to explore this new research area of behavior change interventions for responsible data science.