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Authors: Poulami Sinhamahapatra ; Lena Heidemann ; Maureen Monnet and Karsten Roscher

Affiliation: Fraunhofer IKS, Germany

Keyword(s): Interpretability, Global Explainability, Classification, Prototype-Based Learning.

Abstract: Explaining black-box Artificial Intelligence (AI) models is a cornerstone for trustworthy AI and a prerequisite for its use in safety critical applications such that AI models can reliably assist humans in critical decisions. However, instead of trying to explain our models post-hoc, we need models which are interpretable-by-design built on a reasoning process similar to humans that exploits meaningful high-level concepts such as shapes, texture or object parts. Learning such concepts is often hindered by its need for explicit specification and annotation up front. Instead, prototype-based learning approaches such as ProtoPNet claim to discover visually meaningful prototypes in an unsupervised way. In this work, we propose a set of properties that those prototypes have to fulfill to enable human analysis, e.g. as part of a reliable model assessment case, and analyse such existing methods in the light of these properties. Given a ‘Guess who?’ game, we find that these prototypes still have a long way ahead towards definite explanations. We quantitatively validate our findings by conducting a user study indicating that many of the learnt prototypes are not considered useful towards human understanding. We discuss about the missing links in the existing methods and present a potential real-world application motivating the need to progress towards truly human-interpretable prototypes. (More)

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Paper citation in several formats:
Sinhamahapatra, P.; Heidemann, L.; Monnet, M. and Roscher, K. (2023). Towards Human-Interpretable Prototypes for Visual Assessment of Image Classification Models. In Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 5: VISAPP; ISBN 978-989-758-634-7; ISSN 2184-4321, SciTePress, pages 878-887. DOI: 10.5220/0011894900003417

@conference{visapp23,
author={Poulami Sinhamahapatra. and Lena Heidemann. and Maureen Monnet. and Karsten Roscher.},
title={Towards Human-Interpretable Prototypes for Visual Assessment of Image Classification Models},
booktitle={Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 5: VISAPP},
year={2023},
pages={878-887},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011894900003417},
isbn={978-989-758-634-7},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 5: VISAPP
TI - Towards Human-Interpretable Prototypes for Visual Assessment of Image Classification Models
SN - 978-989-758-634-7
IS - 2184-4321
AU - Sinhamahapatra, P.
AU - Heidemann, L.
AU - Monnet, M.
AU - Roscher, K.
PY - 2023
SP - 878
EP - 887
DO - 10.5220/0011894900003417
PB - SciTePress

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