Behavior Matters: An Alternative Perspective on Promoting Responsible Data Science

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.

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Publication Details

Journal
Proceedings of the ACM on Human-Computer Interaction
Published
2025-05-02
DOI
https://doi.org/10.1145/3710932
Citations
1
Primary Topic
Ethics and Social Impacts of AI
Type
article
Field-Weighted Citation Impact
2.37
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article

Behavior Matters: An Alternative Perspective on Promoting Responsible Data Science

Leilani Battle, Emily Wall, Yuichi Shoda, Dong Zhang et al.
1 citations
Proceedings of the ACM on Human-Computer Interaction
Ethics and Social Impacts of AI
2.37
article

Behavior Matters: An Alternative Perspective on Promoting Responsible Data Science

Leilani Battle, Emily Wall, Yuichi Shoda, Dong Zhang, Ameya Patil
article en
1 citations

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.

Proceedings of the ACM on Human-Computer InteractionVol. 9(2)
Emory University (US), University of Washington (US)
Peace, Justice and strong institutions, Reduced inequalities
Openalex Percentile: Top 12%
Ethics and Social Impacts of AI
2.37
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