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Rethinking Personas for Fairness: Algorithmic Transparency and Accountability in Data-Driven Personas

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Artificial Intelligence in HCI (HCII 2020)

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Abstract

Algorithmic fairness criteria for machine learning models are gathering widespread research interest. They are also relevant in the context of data-driven personas that rely on online user data and opaque algorithmic processes. Overall, while technology provides lucrative opportunities for the persona design practice, several ethical concerns need to be addressed to adhere to ethical standards and to achieve end user trust. In this research, we outline the key ethical concerns in data-driven persona generation and provide design implications to overcome these ethical concerns. Good practices of data-driven persona development include (a) creating personas also from outliers (not only majority groups), (b) using data to demonstrate diversity within a persona, (c) explaining the methods and their limitations as a form of transparency, and (d) triangulating the persona information to increase truthfulness.

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Notes

  1. 1.

    https://www.acm.org/binaries/content/assets/public-policy/2017_joint_statement_algorithms.pdf.

  2. 2.

    https://scikit-learn.org/stable/.

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Authors and Affiliations

  1. Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha, Qatar

    Joni Salminen, Soon-gyo Jung, Shammur A. Chowdhury & Bernard J. Jansen

  2. University of Turku, Turku, Finland

    Joni Salminen

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  1. Joni Salminen
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  2. Soon-gyo Jung
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  3. Shammur A. Chowdhury
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  1. Siemens, Princeton, USA

    Helmut Degen

  2. University of Central Florida, Orlando, FL, USA

    Lauren Reinerman-Jones

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Salminen, J., Jung, Sg., Chowdhury, S.A., Jansen, B.J. (2020). Rethinking Personas for Fairness: Algorithmic Transparency and Accountability in Data-Driven Personas. In: Degen, H., Reinerman-Jones, L. (eds) Artificial Intelligence in HCI. HCII 2020. Lecture Notes in Computer Science(), vol 12217. Springer, Cham. https://doi.org/10.1007/978-3-030-50334-5_6

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