Abstract
Feature attribution explanation (FAE) method, which reveals the contribution of each input feature to the model's output, is one of the most popular explainable artificial intelligence techniques. To assess the quality of explanations provided by FAE methods, various metrics spanning multiple dimensions have been introduced. However, current FAE approaches often prioritize faithfulness of their explanations, neglecting other crucial aspects. To address this issue, we define the construction of a FAE explainable model as a multi-objective learning problem and propose a framework that simultaneously considers multiple quality metrics during FAE explanation generation. Our experimental results demonstrate that our approach outperforms existing FAE methods in terms of faithfulness, sensitivity, and complexity. Moreover, our method has better diversity and the capacity to offer different explanations for different stakeholders.This paper for the Hot-off-the-Press track at GECCO 2024 summarizes the work Ziming Wang, Changwu Huang, Yun Li, and Xin Yao: Multi-objective Feature Attribution Explanation for Explainable Machine Learning published in ACM Transactions on Evolutionary Learning and Optimization [5].
Original language | English |
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Title of host publication | GECCO '24 Companion: Proceedings of the 2024 Genetic and Evolutionary Computation Conference Companion |
Publisher | Association for Computing Machinery, Inc |
Pages | 61-62 |
Number of pages | 2 |
ISBN (Electronic) | 9798400704956 |
DOIs | |
Publication status | Published - 1 Aug 2024 |
Event | 2024 Genetic and Evolutionary Computation Conference Companion, GECCO 2024 Companion - Melbourne, Australia Duration: 14 Jul 2024 → 18 Jul 2024 |
Conference
Conference | 2024 Genetic and Evolutionary Computation Conference Companion, GECCO 2024 Companion |
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Country/Territory | Australia |
City | Melbourne |
Period | 14/07/24 → 18/07/24 |
Bibliographical note
Publisher Copyright: © 2024 Copyright held by the owner/author(s).Funding
This work was supported by the National Natural Science Foundation of China (Grant No. 62250710682), the Guangdong Provincial Key Laboratory (Grant No. 2020B121201001), the Program for Guangdong Introducing Innovative and Entrepreneurial Teams (Grant No.2017ZT07X386), and the Research Institute of Trustworthy Autonomous Systems.
Keywords
- explainable machine learning
- feature attribution explanations
- multi-objective evolutionary algorithms
- multi-objective learning