Sensemaking is the iterative process of identifying, extracting, and explaining insights from data, where each iteration is referred to as the “sensemaking loop.” However, little is known about how sensemaking behavior evolves from exploration and explanation during this process. This gap limits our ability to understand the full scope of sensemaking, which in turn inhibits the design of tools that support the process. We contribute the first mixed-method to characterize how sensemaking evolves within computational notebooks. We study 2,574 Jupyter notebooks mined from GitHub by identifying data science notebooks that have undergone significant iterations, presenting a regression model that automatically characterizes sensemaking activity, and using this regression model to calculate and analyze shifts in activity across GitHub versions. Our results show that notebook authors participate in various sensemaking tasks over time, such as annotation, branching analysis, and documentation. We use our insights to recommend extensions to current notebook environments.

Recent research at the intersection of AI explainability and fairness has focused on how explanations can improve human-plus-AI task performance as assessed by fairness measures.We propose to characterize what constitutes an explanation that is itself "fair" -an explanation that does not adversely impact specific populations.We formulate a novel evaluation method of "fair explanations" using not just accuracy and label time, but also psychological impact of explanations on different user groups across many metrics (mental discomfort, stereotype activation, and perceived workload).We apply this method in the context of content moderation of potential hate speech, and its differential impact on Asian vs. non-Asian proxy moderators, across explanation approaches (saliency map and counterfactual explanation).We find that saliency maps generally perform better and show less evidence of disparate impact (group) and individual unfairness than counterfactual explanations.1 Content warning: This paper contains examples of hate speech and racially discriminatory language.The authors do not support such content.Please consider your risk of discomfort carefully before continuing reading!

Deriving actionable insights from data requires expertise in both data science as well as the specific application domain. This need for domain-specific knowledge often necessitates engaging an interdisciplinary team rather than an individual for most realistic data science problems in domains such as finance, biology, or drug discovery. This, in turn, requires effective collaboration between team members. This paper seeks to understand common themes in how such multi-disciplinary teams communicate to accomplish their analytical goals. We conduct an interview study with 15 professional data scientists working in small to large organizations in fields ranging from bioinformatics to accounting. Communication between individuals in these teams depends on their team structure and expertise. Data scientists specifically adapt their tools to communicate with team members with different types of domain knowledge. We discuss the strengths and weaknesses of these approaches for supporting communication in multi-disciplinary environments.