Bias by Default?
Systems have biases. Their interfaces naturally guide a user toward specific patterns of action. For example, modern word-processors and spreadsheets are both capable of handling word wrapping, checking spelling and calculating formulas. You could write a paper in a spreadsheet or could do simple business modeling in a word-processor. However, their interfaces naturally communicate the function for which they are designed. Visual analytic interfaces also have biases. We outline why simple Markov models are a plausible tool for investigating that bias, even prior to user interactions, and how they might be applied to understand a priori system biases. We also discuss some anticipated difficulties in such modeling and touch briefly on what some Markov model extensions might provide.
This research was sponsored by the Analysis in Motion Initiative at the Pacific Northwest National Laboratory. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the U.S. Government.
- 1.Ballard DH (1997) An introduction to national computation. MIT Press, Cambridge, MAGoogle Scholar
- 2.Cook K, Cramer N, Israel D, Wolverton M, Bruce J, Burtner R, Endert A (2015) Mixed-initiative visual analytics using task-driven recommendations. In: 2015 IEEE conference on visual analytics science and technology (VAST). IEEE, New York, pp 9–16Google Scholar
- 3.Cook KA, Thomas JJ (2005) Illuminating the path: the research and development agenda for visual analytics. IEEE Computer Society, Los Alamitos, CAGoogle Scholar
- 5.Dunne C, Henry Riche N, Lee B, Metoyer R, Robertson G (2012) Graphtrail: analyzing large multivariate, heterogeneous networks while supporting exploration history. In: Proceedings of the SIGCHI conference on human factors in computing systems. ACM, New York, pp 1663–1672Google Scholar
- 11.Gapminder Foundation. Gapminder.org: Geography. https://www.gapminder.org/data/geo/ (2018). Accessed 01 Apr 2018
- 12.Gapminder Foundation. Gapminder.org: List of indicators in gapminder world. https://www.gapminder.org/data/ (2018). Accessed 01 Apr 2018
- 14.Jankun-Kelly T (2008) Using visualization process graphs to improve visualization exploration. In: International provenance and annotation workshop. Springer, Berlin, pp 78–91Google Scholar
- 17.Wall E, Blaha L, Franklin L, Endert A (2017) Warning, bias may occur: a proposed approach to detecting cognitive bias in interactive visual analytics. In: IEEE visual analytics science and technology (VAST). IEEE, New YorkGoogle Scholar
- 18.Wall E, Blaha L, Paul CL, Cook K, Endert A (2017) Four perspectives on human bias in visual analytics. In: Ellis G (ed) Cognitive biases in visualizations, Chap. 3. Springer, BerlinGoogle Scholar
- 20.Wattenberg M (1999) Visualizing the stock market. In: CHI’99 extended abstracts on human factors in computing systems. ACM, New York, pp 188–189Google Scholar