A4 Vertaisarvioitu artikkeli konferenssijulkaisussa
Rethinking personas for fairness: Algorithmic transparency and accountability in data-driven personas
Tekijät: Joni Salminen, Soon-gyo Jung, Shammur A. Chowdhury, Bernard J. Jansen
Toimittaja: Helmut Degen, Lauren Reinerman-Jones
Konferenssin vakiintunut nimi: International Conference on Human-Computer Interaction
Kustantaja: Springer
Kustannuspaikka: Cham
Julkaisuvuosi: 2020
Journal: International Conference on Human-Computer Interaction
Kokoomateoksen nimi: 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
Tietokannassa oleva lehden nimi: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Sarjan nimi: Lecture Notes in Computer Science
Vuosikerta: 12217
Aloitussivu: 82
Lopetussivu: 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
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.
