Rethinking personas for fairness: Algorithmic transparency and accountability in data-driven personas

Joni Salminen; Soon gyo Jung; Shammur A. Chowdhury; Bernard J. Jansen

doi:10.1007/978-3-030-50334-5_6

Rethinking personas for fairness: Algorithmic transparency and accountability in data-driven personas

Joni Salminen^*, Soon gyo Jung, Shammur A. Chowdhury, Bernard J. Jansen

^*Corresponding author for this work

Qatar Computing Research Institute

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Citations (Scopus)

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.

Original language	English
Title of host publication	Artificial Intelligence in HCI - 1st International Conference, AI-HCI 2020, Held as Part of the 22nd HCI International Conference, HCII 2020, Proceedings
Editors	Helmut Degen, Lauren Reinerman-Jones
Publisher	Springer
Pages	82-100
Number of pages	19
ISBN (Print)	9783030503338
DOIs	https://doi.org/10.1007/978-3-030-50334-5_6
Publication status	Published - 2020
Event	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 - Copenhagen, Denmark Duration: 19 Jul 2020 → 24 Jul 2020

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	12217 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	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
Country/Territory	Denmark
City	Copenhagen
Period	19/07/20 → 24/07/20

Keywords

Algorithms
Data
Ethics
Fairness
Personas
Transparency

Access to Document

10.1007/978-3-030-50334-5_6

Cite this

Salminen, J., Jung, S. G., Chowdhury, S. A., & Jansen, B. J. (2020). Rethinking personas for fairness: Algorithmic transparency and accountability in data-driven personas. In H. Degen, & L. Reinerman-Jones (Eds.), Artificial Intelligence in HCI - 1st International Conference, AI-HCI 2020, Held as Part of the 22nd HCI International Conference, HCII 2020, Proceedings (pp. 82-100). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12217 LNCS). Springer. https://doi.org/10.1007/978-3-030-50334-5_6

Salminen, Joni ; Jung, Soon gyo ; Chowdhury, Shammur A. et al. / Rethinking personas for fairness : Algorithmic transparency and accountability in data-driven personas. Artificial Intelligence in HCI - 1st International Conference, AI-HCI 2020, Held as Part of the 22nd HCI International Conference, HCII 2020, Proceedings. editor / Helmut Degen ; Lauren Reinerman-Jones. Springer, 2020. pp. 82-100 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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Salminen, J, Jung, SG , Chowdhury, SA & Jansen, BJ 2020, Rethinking personas for fairness: Algorithmic transparency and accountability in data-driven personas. in H Degen & L Reinerman-Jones (eds), Artificial Intelligence in HCI - 1st International Conference, AI-HCI 2020, Held as Part of the 22nd HCI International Conference, HCII 2020, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12217 LNCS, Springer, pp. 82-100, 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, Copenhagen, Denmark, 19/07/20. https://doi.org/10.1007/978-3-030-50334-5_6

Rethinking personas for fairness: Algorithmic transparency and accountability in data-driven personas. / Salminen, Joni; Jung, Soon gyo ; Chowdhury, Shammur A. et al.
Artificial Intelligence in HCI - 1st International Conference, AI-HCI 2020, Held as Part of the 22nd HCI International Conference, HCII 2020, Proceedings. ed. / Helmut Degen; Lauren Reinerman-Jones. Springer, 2020. p. 82-100 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12217 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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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.

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ER -

Salminen J, Jung SG , Chowdhury SA , Jansen BJ. Rethinking personas for fairness: Algorithmic transparency and accountability in data-driven personas. In Degen H, Reinerman-Jones L, editors, Artificial Intelligence in HCI - 1st International Conference, AI-HCI 2020, Held as Part of the 22nd HCI International Conference, HCII 2020, Proceedings. Springer. 2020. p. 82-100. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-50334-5_6

Rethinking personas for fairness: Algorithmic transparency and accountability in data-driven personas

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