The Ethics of Data-Driven Personas
Joni O. Salminen, Willemien Froneman, Soon‐gyo Jung, Shammur Absar Chowdhury, Bernard J. Jim Jansen · 2020
Quantitative methods are becoming more common for persona creation, but it is not clear to which extent online data and opaque machine learning algorithms introduce bias at various steps of data-driven persona creation (DDPC) and if these methods violate user rights. In this conceptual analysis, we use Gillespie's framework of algorithmic ethics to analyze DDPC for ethical considerations. We propose five design questions for evaluating the ethics of DDPC. DDPC should demonstrate the diversity of the user base but represent the actual data, be accompanied by explanations of their creation, and mitigate the possibility of unfair decisions.