mlsanityData to 14 Oct 2026

Behavior Matters: An Alternative Perspective on Promoting Responsible Data Science

arXiv 2410.17273 ↗First posted 7 Oct 2024cs.CY cs.HC cs.LGcited by 0

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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.

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.

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References · 15

  1. 2019A Survey on Bias and Fairness in Machine Learningcited by 1,917
  2. 2019Human-AI Collaboration in Data Science: Exploring Data Scientists' Perceptions of Automated AIcited by 79
  3. 2020Human Factors in Model Interpretability: Industry Practices, Challenges, and Needscited by 74
  4. 2020How do Data Science Workers Collaborate? Roles, Workflows, and Toolscited by 64
  5. 2020AutoML to Date and Beyond: Challenges and Opportunitiescited by 63
  6. 2021Socially Responsible AI Algorithms: Issues, Purposes, and Challengescited by 56
  7. 2018Model Selection Techniques -- An Overviewcited by 36
  8. 2020Trust in Data Science: Collaboration, Translation, and Accountability in Corporate Data Science Projectscited by 24
  9. 2021Fits and Starts: Enterprise Use of AutoML and the Role of Humans in the Loopcited by 21
  10. 2021Dissonance Between Human and Machine Understandingcited by 18
  11. 2018Ethical Dimensions of Visualization Researchcited by 10
  12. 2019Tea: A High-level Language and Runtime System for Automating Statistical Analysiscited by 8
3 more
  1. 2021Left, Right, and Gender: Exploring Interaction Traces to Mitigate Human Biasescited by 4
  2. 2022Affective Learning Objectives for Communicative Visualizationscited by 4
  3. 2016Teaching Data Sciencecited by 2

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