IEEE Access (Jan 2022)

Personas for Artificial Intelligence (AI) an Open Source Toolbox

  • Andreas Holzinger,
  • Michaela Kargl,
  • Bettina Kipperer,
  • Peter Regitnig,
  • Markus Plass,
  • Heimo Muller

DOI
https://doi.org/10.1109/ACCESS.2022.3154776
Journal volume & issue
Vol. 10
pp. 23732 – 23747

Abstract

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Personas have successfully supported the development of classical user interfaces for more than two decades by mapping users’ mental models to specific contexts. The rapid proliferation of Artificial Intelligence (AI) applications makes it necessary to create new approaches for future human-AI interfaces. Human-AI interfaces differ from classical human-computer interfaces in many ways, such as gaining some degree of human-like cognitive, self-executing, and self-adaptive capabilities and autonomy, and generating unexpected outputs that require non-deterministic interactions. Moreover, the most successful AI approaches are so-called “black box” systems, where the technology and the machine learning process are opaque to the user and the AI output is far not intuitive. This work shows how the personas method can be adapted to support the development of human-centered AI applications, and we demonstrate this on the example of a medical context. This work is - to our knowledge - the first to provide personas for AI using an openly available Personas for AI toolbox. The toolbox contains guidelines and material supporting persona development for AI as well as templates and pictures for persona visualisation. It is ready to use and freely available to the international research and development community. Additionally, an example from medical AI is provided as a best practice use case. This work is intended to help foster the development of novel human-AI interfaces that will be urgently needed in the near future.

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