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Bias by Default?

A Means for A Priori Interface Measurement
  • Joseph A. CottamEmail author
  • Leslie M. Blaha
Chapter

Abstract

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.

Notes

Acknowledgements

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.

References

  1. 1.
    Ballard DH (1997) An introduction to national computation. MIT Press, Cambridge, MAGoogle Scholar
  2. 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. 3.
    Cook KA, Thomas JJ (2005) Illuminating the path: the research and development agenda for visual analytics. IEEE Computer Society, Los Alamitos, CAGoogle Scholar
  4. 4.
    Dabek F, Caban JJ (2017) A grammar-based approach for modeling user interactions and generating suggestions during the data exploration process. IEEE Trans Visual Comput Graphics 23(1):41–50CrossRefGoogle Scholar
  5. 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
  6. 6.
    Endert A, Fiaux P, North C (2012) Semantic interaction for sensemaking: inferring analytical reasoning for model steering. IEEE Trans Visual Comput Graphics 18(12):2879–2888CrossRefGoogle Scholar
  7. 7.
    Endert A, Ribarsky W, Turkay C, Wong B, Nabney I, Blanco ID, Rossi F (2017) The state of the art in integrating machine learning into visual analytics. Comput Graphics Forum.  https://doi.org/10.1111/cgf.13092CrossRefGoogle Scholar
  8. 8.
    Fitts PM (1954) The information capacity of the human motor system in controlling the amplitude of movement. J Exp Psychol 47(6):381–391CrossRefGoogle Scholar
  9. 9.
    Friedman B (1996) Value-sensitive design. Interactions 3(6):16–23CrossRefGoogle Scholar
  10. 10.
    Friedman B, Nissenbaum H (1996) Bias in computer systems. ACM Trans n Inf Syst (TOIS) 14(3):330–347CrossRefGoogle Scholar
  11. 11.
    Gapminder Foundation. Gapminder.org: Geography. https://www.gapminder.org/data/geo/ (2018). Accessed 01 Apr 2018
  12. 12.
    Gapminder Foundation. Gapminder.org: List of indicators in gapminder world. https://www.gapminder.org/data/ (2018). Accessed 01 Apr 2018
  13. 13.
    Gotz D, Zhou MX (2009) Characterizing users’ visual analytic activity for insight provenance. Inf Visual 8(1):42–55CrossRefGoogle Scholar
  14. 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
  15. 15.
    Kemeny JG, Snell JL (1960) Finite Markov chains, vol 356. van Nostrand, Princeton, NJzbMATHGoogle Scholar
  16. 16.
    Patterson RE, Blaha LM, Grinstein GG, Liggett KK, Kaveney DE, Sheldon KC, Havig PR, Moore JA (2014) A human cognition framework for information visualization. Comput Graphics 42:42–58CrossRefGoogle Scholar
  17. 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. 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
  19. 19.
    Ward MO, Grinstein G, Keim D (2010) Interactive data visualization: foundations, techniques, and applications. CRC Press, Natick, MAzbMATHGoogle Scholar
  20. 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
  21. 21.
    Xu K, Attfield S, Jankun-Kelly T, Wheat A, Nguyen PH, Selvaraj N (2015) Analytic provenance for sensemaking: a research agenda. IEEE Comput Graphics Appl 35(3):56–64CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Pacific Northwest National LaboratoryRichlandUSA

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