Guarded Deep Learning using Scenario-based Modeling

Guy Katz

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Paper

Guarded Deep Learning using Scenario-based Modeling

Topics: Domain-Specific Modeling; Model Execution and Simulation; Model Quality; Modeling for AI Applications; Reasoning about Models

In Proceedings of the 8th International Conference on Model-Driven Engineering and Software Development MODELSWARD - Volume 1, 126-136, 2020 , Valletta, Malta

Author: Guy Katz

Affiliation: The Hebrew University of Jerusalem, Jerusalem, Israel

Keyword(s): Scenario-based Modeling, Behavioral Programming, Machine Learning, Deep Neural Networks.

Abstract: Deep neural networks (DNNs) are becoming prevalent, often outperforming manually-created systems. Unfortunately, DNN models are opaque to humans, and may behave in unexpected ways when deployed. One approach for allowing safer deployment of DNN models calls for augmenting them with hand-crafted override rules, which serve to override decisions made by the DNN model when certain criteria are met. Here, we propose to bring together DNNs and the well-studied scenario-based modeling paradigm, by expressing these override rules as simple and intuitive scenarios. This approach can lead to override rules that are comprehensible to humans, but are also sufficiently expressive and powerful to increase the overall safety of the model. We describe how to extend and apply scenario-based modeling to this new setting, and demonstrate our proposed technique on multiple DNN models.

CC BY-NC-ND 4.0

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Paper citation in several formats:

Katz, G. (2020). Guarded Deep Learning using Scenario-based Modeling. In Proceedings of the 8th International Conference on Model-Driven Engineering and Software Development - MODELSWARD; ISBN 978-989-758-400-8; ISSN 2184-4348, SciTePress, pages 126-136. DOI: 10.5220/0009097601260136

@conference{modelsward20,
author={Guy Katz.},
title={Guarded Deep Learning using Scenario-based Modeling},
booktitle={Proceedings of the 8th International Conference on Model-Driven Engineering and Software Development - MODELSWARD},
year={2020},
pages={126-136},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009097601260136},
isbn={978-989-758-400-8},
issn={2184-4348},
}

TY - CONF

JO - Proceedings of the 8th International Conference on Model-Driven Engineering and Software Development - MODELSWARD
TI - Guarded Deep Learning using Scenario-based Modeling
SN - 978-989-758-400-8
IS - 2184-4348
AU - Katz, G.
PY - 2020
SP - 126
EP - 136
DO - 10.5220/0009097601260136
PB - SciTePress