# Behavior Matters: An Alternative Perspective on Promoting Responsible Data Science

arXiv: https://arxiv.org/abs/2410.17273 · HTML: https://mlsanity.com/paper/2410.17273/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 81 references that are in the map.
- First posted on arXiv 7 Oct 2024.

## Builds on (most cited first, 15 of 15)

- [A Survey on Bias and Fairness in Machine Learning](https://mlsanity.com/paper/1908.09635/) (2019)
- [Human-AI Collaboration in Data Science: Exploring Data Scientists' Perceptions of Automated AI](https://mlsanity.com/paper/1909.02309/) (2019)
- [Human Factors in Model Interpretability: Industry Practices, Challenges, and Needs](https://mlsanity.com/paper/2004.11440/) (2020)
- [How do Data Science Workers Collaborate? Roles, Workflows, and Tools](https://mlsanity.com/paper/2001.06684/) (2020)
- [AutoML to Date and Beyond: Challenges and Opportunities](https://mlsanity.com/paper/2010.10777/) (2020)
- [Socially Responsible AI Algorithms: Issues, Purposes, and Challenges](https://mlsanity.com/paper/2101.02032/) (2021)
- [Model Selection Techniques -- An Overview](https://mlsanity.com/paper/1810.09583/) (2018)
- [Trust in Data Science: Collaboration, Translation, and Accountability in Corporate Data Science Projects](https://mlsanity.com/paper/2002.03389/) (2020)
- [Fits and Starts: Enterprise Use of AutoML and the Role of Humans in the Loop](https://mlsanity.com/paper/2101.04296/) (2021)
- [Dissonance Between Human and Machine Understanding](https://mlsanity.com/paper/2101.07337/) (2021)
- [Ethical Dimensions of Visualization Research](https://mlsanity.com/paper/1811.07271/) (2018)
- [Tea: A High-level Language and Runtime System for Automating Statistical Analysis](https://mlsanity.com/paper/1904.05387/) (2019)
- [Left, Right, and Gender: Exploring Interaction Traces to Mitigate Human Biases](https://mlsanity.com/paper/2108.03536/) (2021)
- [Affective Learning Objectives for Communicative Visualizations](https://mlsanity.com/paper/2208.04078/) (2022)
- [Teaching Data Science](https://mlsanity.com/paper/1604.07397/) (2016)

## Abstract (arXiv)

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.