Introducing a Multi-Perspective xAI Tool for Better Model Explainability
Authors: 
Marek Pawlicki, Damian Puchalski, Sebastian Szelest, Aleksandra Pawlicka, Rafał Kozik, Michał ChoraśAuthors Info & Claims
Marek Pawlicki, Damian Puchalski, Sebastian Szelest, Aleksandra Pawlicka, Rafał Kozik, Michał ChoraśAuthors Info & ClaimsARES '24: Proceedings of the 19th International Conference on Availability, Reliability and Security
Article No.: 95, Pages 1 - 8
Abstract
This paper introduces an innovative tool equipped with a multi-perspective, user-friendly dashboard designed to enhance the explainability of AI models, particularly in cybersecurity. By enabling users to select data samples and apply various xAI methods, the tool provides insightful views into the decision-making processes of AI systems. These methods offer diverse perspectives and deepen the understanding of how models derive their conclusions, thus demystifying the "black box" of AI. The tool’s architecture facilitates easy integration with existing ML models, making it accessible to users regardless of their technical expertise. This approach promotes transparency and fosters trust in AI applications by aligning decision-making with domain knowledge and mitigating potential biases.
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Published In
July 2024
2032 pages
ISBN:9798400717185
DOI:10.1145/3664476
Copyright © 2024 ACM.
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Published: 30 July 2024
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ARES 2024
ARES 2024: The 19th International Conference on Availability, Reliability and Security
July 30 - August 2, 2024
Vienna, Austria
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