Deceptive AI Explanations: Creation and Detection

Johannes Schneider, Christian Meske, Michalis Vlachos

2022

Abstract

Artificial intelligence (AI) comes with great opportunities but can also pose significant risks. Automatically generated explanations for decisions can increase transparency and foster trust, especially for systems based on automated predictions by AI models. However, given, e.g., economic incentives to create dishonest AI, to what extent can we trust explanations? To address this issue, our work investigates how AI models (i.e., deep learning, and existing instruments to increase transparency regarding AI decisions) can be used to create and detect deceptive explanations. As an empirical evaluation, we focus on text classification and alter the explanations generated by GradCAM, a well-established explanation technique in neural networks. Then, we evaluate the effect of deceptive explanations on users in an experiment with 200 participants. Our findings confirm that deceptive explanations can indeed fool humans. However, one can deploy machine learning (ML) methods to detect seemingly minor deception attempts with accuracy exceeding 80% given sufficient domain knowledge. Without domain knowledge, one can still infer inconsistencies in the explanations in an unsupervised manner, given basic knowledge of the predictive model under scrutiny.

Paper Citation

in Harvard Style

Schneider J., Meske C. and Vlachos M. (2022). Deceptive AI Explanations: Creation and Detection. In Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, ISBN 978-989-758-547-0, pages 44-55. DOI: 10.5220/0010768300003116

in Bibtex Style

@conference{icaart22,
author={Johannes Schneider and Christian Meske and Michalis Vlachos},
title={Deceptive AI Explanations: Creation and Detection},
booktitle={Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,},
year={2022},
pages={44-55},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010768300003116},
isbn={978-989-758-547-0},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,
TI - Deceptive AI Explanations: Creation and Detection
SN - 978-989-758-547-0
AU - Schneider J.
AU - Meske C.
AU - Vlachos M.
PY - 2022
SP - 44
EP - 55
DO - 10.5220/0010768300003116