Quantitative data and algorithms are becoming more common for persona creation, but it is not clear to which extent this data and opaque machine learning algorithms introduce bias at various steps of data-driven persona creation (DDPC) and/or violate user rights.
In this conceptual work, led by Joni Salminen, we use Gillespie’s framework of algorithmic ethics to analyze DDPC for ethical considerations.
We formulate five design questions for evaluating the ethics of DDPC. Data-driven personas should demonstrate the diversity of the user base, be transparent in their creation, minimize the possibility of unfair decisions, and represent the actual data.
Results also may be applicable to personas derived via other means.
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Salminen, J., Froneman, W., Jung, S.G., Chowdhury. S., and Jansen, B. J.(2020) The Ethics of Data-Driven Personas. ACM CHI Conference on Human Factors in Computing Systems (CHI’20) (Extended Abstract), Honolulu, HI, USA. 25–30 April, 1-9.