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Persona Transparency: Analyzing the Impact of Explanations on Perceptions of Data-Driven Persona

Persona Transparency: Analyzing the Impact of Explanations on Perceptions of Data-Driven Personas

Persona Transparency: Analyzing the Impact of Explanations on Perceptions of Data-Driven Personas

Computational techniques are becoming more common in persona development. However, users of personas may question the information in persona profiles because they are unsure of how it was created.

This problem is especially vexing for data-driven personas because their creation is an opaque algorithmic process. 

In this research, we analyze the effect of increased transparency – i.e., explanations of how the information in data-driven personas was produced – on user perceptions. 

We find that higher transparency through these explanations increases the perceived completeness and clarity of the personas. 

Contrary to  our hypothesis, the perceived credibility of the personas decreases with the increased transparency, possibly due to the technical complexity of the persona profiles disrupting the facade of the personas being real people. 

This finding suggests that explaining the algorithmic process of data-driven persona creation involves a “transparency trade-off”. 

We also find that the gender of the persona affects the perceptions, with transparency increasing perceived completeness and empathy of the female persona, but not for the male persona. Therefore, transparency may specifically assist in the acceptance of female personas. 

We provide practical implications for persona creators regarding transparency in persona profiles.

Read Full Research

Salminen, J., Santos, J., Jung, S. G., Eslami, M. and Jansen, B. J. (2020) Persona Transparency: Analyzing the Impact of Explanations on Perceptions of Data-Driven PersonasInternational Journal of Human-Computer Interaction, 36(8), 788-800.

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Data-Driven Personas Persona Analytics Persona Design Persona System Personas

Explaining Data-Driven Personas to End Users

Explaining Data-Driven Personas to End Users
Explaining Data-Driven Personas to End Users

Enabled by digital user data and algorithms, persona user interfaces (UI) are moving to digital formats. However, algorithms and user data, if left unexplained to end-users, might leave data-driven personas (DDPs) difficult to understand and trust.

This is because the data and the way it is processed are complex and not self-evident, requiring explanations of the DDP information and UIs.

In this research, we provide a proof of concept for adding transparency to DDP using a real system UI.

Furthermore, we demonstrate ways to add breakdown information that can help alleviate user stereotyping associated with the use of personas.

Read the full article

Jung, S.G., Salminen, J., and Jansen, B. J. (2020) Explaining Data Driven Personas to End Users. Proceedings of the Workshop on Explainable Smart Systems for Algorithmic Transparency in Emerging Technologies co-located with 25th International Conference on Intelligent User Interfaces (IUI 2020). Cagliari, Italy, 17 March 17.

Read more about data-driven personas

What is a Data-Driven Persona?

Introduction to Data-Driven Personas

Giving Faces to Data by Creating Data-Driven Personas

Benefits of Data-Driven Personas

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