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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. 

In this paper, we present a new DBMS performance benchmark that cansimulateuser exploration with any specified dashboard design made of standard visualization and interaction components. The distinguishing feature of our SImulation-BAsed (or SIMBA) benchmark is its ability tomodel user analysis goalsas a set of SQL queries to be generated through a valid sequence of user interactions, as well asmeasure the completion of analysis goalsby testing for equivalence between the user's previous queries and their goal queries. In this way, the SIMBA benchmark can simulate how an analyst opportunistically searches for interesting insights at the beginning of an exploration session and eventually hones in on specific goals towards the end. To demonstrate the versatility of the SIMBA benchmark, we use it to test the performance of four DBMSs with six different dashboard specifications and compare our results with IDEBench. Our results show how goal-driven simulation can reveal gaps in DBMS performance missed by existing benchmarking methods and across a range of data exploration scenarios.