TY - GEN
T1 - Rethinking personas for fairness
T2 - 1st International Conference on Artificial Intelligence in HCI, AI-HCI 2020, held as part of the 22nd International Conference on Human-Computer Interaction, HCII 2020
AU - Salminen, Joni
AU - Jung, Soon gyo
AU - Chowdhury, Shammur A.
AU - Jansen, Bernard J.
N1 - Publisher Copyright:
© Springer Nature Switzerland AG 2020.
PY - 2020
Y1 - 2020
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85088753434&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85088753434&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-50334-5_6
DO - 10.1007/978-3-030-50334-5_6
M3 - Conference contribution
AN - SCOPUS:85088753434
SN - 9783030503338
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 82
EP - 100
BT - Artificial Intelligence in HCI - 1st International Conference, AI-HCI 2020, Held as Part of the 22nd HCI International Conference, HCII 2020, Proceedings
A2 - Degen, Helmut
A2 - Reinerman-Jones, Lauren
PB - Springer
Y2 - 19 July 2020 through 24 July 2020
ER -