Letting Go of the Numbers: Measuring AI Trustworthiness

Carol Smith

2024

Abstract

AI systems need to be designed to work with, and for, people. A person’s willingness to trust a particular system is based on their expectations of the system’s behavior. Their trust is complex, transient, and personal – it cannot easily be measured. However, an AI system’s trustworthiness can be measured. A trustworthy AI system demonstrates that it will fulfill its promise by providing evidence that it is dependable in the context of use, and the end user has awareness of its capabilities during use. We can measure reliability and instrument systems to monitor usage (or lack thereof) quantitatively. However, AI’s potential is bound to perceptions of its trustworthiness, which requires qualitative measures to fully ascertain. Doing AI well requires a reset – letting go of (some of) the numbers and learning new methods that provide a more complete assessment of the system.

Paper Citation

in Harvard Style

Smith C. (2024). Letting Go of the Numbers: Measuring AI Trustworthiness. In Proceedings of the 13th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM; ISBN 978-989-758-684-2, SciTePress, pages 11-12. DOI: 10.5220/0012644300003654

in Bibtex Style

@conference{icpram24,
author={Carol Smith},
title={Letting Go of the Numbers: Measuring AI Trustworthiness},
booktitle={Proceedings of the 13th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM},
year={2024},
pages={11-12},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012644300003654},
isbn={978-989-758-684-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 13th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM
TI - Letting Go of the Numbers: Measuring AI Trustworthiness
SN - 978-989-758-684-2
AU - Smith C.
PY - 2024
SP - 11
EP - 12
DO - 10.5220/0012644300003654
PB - SciTePress