Location
Hilton Waikoloa Village, Hawaii
Event Website
https://hicss.hawaii.edu/
Start Date
7-1-2025 12:00 AM
End Date
10-1-2025 12:00 AM
Description
Explainable Artificial Intelligence (XAI) plays a crucial role in enhancing the transparency and interpretability of Machine Learning (ML) models, especially in sensitive domains like Facial Emotion Recognition (FER). This paper evaluates the effectiveness of the model-agnostic methods SHAP (Shapley Additive Explanations) and LIME (Local Interpretable Model-agnostic Explanations) in explaining ML decision-making for FER. Leveraging two established facial emotion databases, FER 2013 and RAF-DB, our research identifies key facial features of ML model predictions. Our results indicate that SHAP offers more consistent and reliable visualizations than LIME, effectively emphasizing critical regions such as the mouth, eyes, and cheeks, which align with the facial Action Units outlined by the Facial Action Coding System (FACS). This alignment enhances model interpretability, demonstrating how XAI can reconcile accuracy with transparency to foster the development of trustworthy AI systems in FER. Our study also shows that in complex domains like FER, XAI methods alone are insufficient; expert interpretation is crucial for applying insights from XAI visualizations, underscoring the need for interdisciplinary research to advance future studies in complex domains.
Recommended Citation
Lorch, Selina; Gebele, Jens; and Brune, Philipp, "Towards Trustworthy AI: Evaluating SHAP and LIME for Facial Emotion Recognition" (2025). Hawaii International Conference on System Sciences 2025 (HICSS-58). 4.
https://aisel.aisnet.org/hicss-58/st/trustworthy_ai/4
Towards Trustworthy AI: Evaluating SHAP and LIME for Facial Emotion Recognition
Hilton Waikoloa Village, Hawaii
Explainable Artificial Intelligence (XAI) plays a crucial role in enhancing the transparency and interpretability of Machine Learning (ML) models, especially in sensitive domains like Facial Emotion Recognition (FER). This paper evaluates the effectiveness of the model-agnostic methods SHAP (Shapley Additive Explanations) and LIME (Local Interpretable Model-agnostic Explanations) in explaining ML decision-making for FER. Leveraging two established facial emotion databases, FER 2013 and RAF-DB, our research identifies key facial features of ML model predictions. Our results indicate that SHAP offers more consistent and reliable visualizations than LIME, effectively emphasizing critical regions such as the mouth, eyes, and cheeks, which align with the facial Action Units outlined by the Facial Action Coding System (FACS). This alignment enhances model interpretability, demonstrating how XAI can reconcile accuracy with transparency to foster the development of trustworthy AI systems in FER. Our study also shows that in complex domains like FER, XAI methods alone are insufficient; expert interpretation is crucial for applying insights from XAI visualizations, underscoring the need for interdisciplinary research to advance future studies in complex domains.
https://aisel.aisnet.org/hicss-58/st/trustworthy_ai/4