A4 Refereed article in a conference publication
Rethinking personas for fairness: Algorithmic transparency and accountability in data-driven personas
Authors: Joni Salminen, Soon-gyo Jung, Shammur A. Chowdhury, Bernard J. Jansen
Editors: Helmut Degen, Lauren Reinerman-Jones
Conference name: International Conference on Human-Computer Interaction
Publisher: Springer
Publishing place: Cham
Publication year: 2020
Journal: International Conference on Human-Computer Interaction
Book title : Artificial Intelligence in HCI First International Conference, AI-HCI 2020, Held as Part of the 22nd HCI International Conference, HCII 2020, Copenhagen, Denmark, July 19–24, 2020, Proceedings
Journal name in source: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Series title: Lecture Notes in Computer Science
Volume: 12217
First page : 82
Last page: 100
ISBN: 978-3-030-50333-8
eISBN: 978-3-030-50334-5
ISSN: 0302-9743
DOI: https://doi.org/10.1007/978-3-030-50334-5_6(external)
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.