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Paper Type
ERF
Description
The lack of explainability remains a critical challenge to the widespread adoption of artificial intelligence (AI) in many fields. “Understanding brings in trust”, while machine-learning models offer superior prediction accuracy, understanding the underlying logic is equally important to foster trust in these models. In this paper, we present eXplainable AI (XAI) as a solution to this challenge. Our research focuses on three key aspects of XAI: mathematics, humanities and social sciences, and practical applications. We demonstrated the feasibility of XAI through the use of artificially-constructed and model-derived ground truth, and verified performances of different XAIs. We also explored three dimensions of explainable consistency and emphasized the significance of human-machine consistency. Finally, we applied our research to a real-world scenario by cooperating with a national bank in China. Our findings highlight that XAI is both mathematically and practically meaningful, but more efforts need to be dedicated to this human-machine communication field.
Paper Number
1909
Recommended Citation
He, Yuanqi; Gu, Bin; Li, Chunxiao; Yan, Lingling; and Ding, Yanqing, "Black-box Models’ Explainability: A Theoretical and Practical Perspective" (2023). AMCIS 2023 Proceedings. 23.
https://aisel.aisnet.org/amcis2023/conf_theme/conf_theme/23
Black-box Models’ Explainability: A Theoretical and Practical Perspective
The lack of explainability remains a critical challenge to the widespread adoption of artificial intelligence (AI) in many fields. “Understanding brings in trust”, while machine-learning models offer superior prediction accuracy, understanding the underlying logic is equally important to foster trust in these models. In this paper, we present eXplainable AI (XAI) as a solution to this challenge. Our research focuses on three key aspects of XAI: mathematics, humanities and social sciences, and practical applications. We demonstrated the feasibility of XAI through the use of artificially-constructed and model-derived ground truth, and verified performances of different XAIs. We also explored three dimensions of explainable consistency and emphasized the significance of human-machine consistency. Finally, we applied our research to a real-world scenario by cooperating with a national bank in China. Our findings highlight that XAI is both mathematically and practically meaningful, but more efforts need to be dedicated to this human-machine communication field.
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Conference Theme