Designing Breadth-Oriented Data Exploration for Mitigating Cognitive Biases

Abstract

Exploratory data analysis involves making a series of complex decisions: what should I explore? what questions should I ask? As users do not have good knowledge about the data they are exploring, making these decisions is non-trivial. In making these decisions, heuristics are often applied, potentially causing a biased exploration path. While breadth-oriented data exploration presents a promising solution to rectifying a biased exploration path, how to design such systems is yet to be explored. In this Chapter, we propose three considerations in designing systems that support breadth-oriented data exploration. To demonstrate the utility of these design considerations, we describe a hypothetical breadth-oriented system. We argue that these design considerations pave the way for understanding how breadth-oriented exploration mitigates biases in exploratory data analysis.