Designing Breadth-Oriented Data Exploration for Mitigating Cognitive Biases
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.
- 1.Chau DH, Kittur A, Hong JI, Faloutsos C (2011) Apolo: making sense of large network data by combining rich user interaction and machine learning. In: Proceedings of the SIGCHI conference on human factors in computing systems, ACM, pp 167–176Google Scholar
- 2.Dheeru D, Karra Taniskidou E (2017) UCI machine learning repository. http://archive.ics.uci.edu/ml/datasets/auto+mpg
- 3.Gotz D, Sun S, Cao N (2016) Adaptive contextualization: combating bias during high-dimensional visualization and data selection. In: Proceedings of the 21st international conference on intelligent user interfaces, ACM, pp 85–95Google Scholar
- 7.Perer A, Shneiderman B (2008) Systematic yet flexible discovery: Guiding domain experts through exploratory data analysis. In: Proceedings of the 13th international conference on intelligent user interfaces, ACM, pp 109–118Google Scholar
- 12.Van Ham F, Perer A (2009) ?search, show context, expand on demand?: Supporting large graph exploration with degree-of-interest. IEEE Trans Visualization Comput Graphics 15(6)Google Scholar