Navigating the Trade-Off Between Explainability and Privacy

Johanna Schmidt, Verena Pietsch, Martin Nocker, Michael Rader, Alessio Montuoro

2024

Abstract

Understanding the rationale behind complex AI decisions becomes increasingly vital as AI evolves. Explainable AI technologies are pivotal in demystifying these decisions, offering methods and tools to interpret and communicate the reasoning behind AI-driven outcomes. However, the rise of Explainable AI is juxtaposed with the imperative to protect sensitive data, leading to the integration of encryption techniques in AI development. This paper explores the intricate coexistence of explainability and encryption in AI, presenting a dilemma where the quest for transparency clashes with the imperative to secure sensitive information. The contradiction is particularly evident in methods like homomorphic encryption, which, while ensuring data security, complicates the provision of clear and interpretable explanations for AI decisions. The discussion delves into the conflicting goals of these approaches, surveying the use of privacy-preserving methods in Explainable AI and identifying potential directions for future research. Contributions include a comprehensive survey of privacy considerations in current Explainable AI approaches, an exemplary use case demonstrating visualization techniques for explainability in secure environments, and identifying avenues for future work.

Paper Citation

in Harvard Style

Schmidt J., Pietsch V., Nocker M., Rader M. and Montuoro A. (2024). Navigating the Trade-Off Between Explainability and Privacy. In Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 1: IVAPP; ISBN 978-989-758-679-8, SciTePress, pages 726-733. DOI: 10.5220/0012472200003660

in Bibtex Style

@conference{ivapp24,
author={Johanna Schmidt and Verena Pietsch and Martin Nocker and Michael Rader and Alessio Montuoro},
title={Navigating the Trade-Off Between Explainability and Privacy},
booktitle={Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 1: IVAPP},
year={2024},
pages={726-733},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012472200003660},
isbn={978-989-758-679-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 1: IVAPP
TI - Navigating the Trade-Off Between Explainability and Privacy
SN - 978-989-758-679-8
AU - Schmidt J.
AU - Pietsch V.
AU - Nocker M.
AU - Rader M.
AU - Montuoro A.
PY - 2024
SP - 726
EP - 733
DO - 10.5220/0012472200003660
PB - SciTePress