## 1 Introduction  
People with type 1 diabetes (T1D) often experience unexplained glycemic excursions that deteriorate glucose control. T1D is an autoimmune disorder caused by the destruction of pancreatic beta cells that produce insulin, leading people with T1D to be dependent on exogenous insulin administration to maintain their blood glucose concentrations within a target range. An obscure disturbance to glycemia is acute psychological stress (APS), which can worsen glucose control in people with T1D through physiological and endocrine stress responses that cause hyperglycemia, or high glucose concentrations [1]. Prolonged hyperglycemia can lead to the development of complications that significantly impair quality of life and reduce life expectancy [2]. Detecting APS is important for mitigating the glycemic effects of APS and maintaining tight glycemic control. It is challenging to mitigate the glycemic effects of APS using automated insulin delivery (AID) systems that rely solely on glucose measurements to modulate insulin delivery. AID systems integrate insulin pumps, continuous glucose monitors, and control algorithms to automatically modulate delivery of rapid acting insulin analogues. Using glucose measurements solely for feedback presents a challenge because elevated glucose levels can be caused by other stimuli, such as meals or anaerobic physical activities, in addition to APS [3, 4, 5, 6, 7]. APS detection using readily measurable physiological variables is an important topic in affective computing [8, 9, 10]. Numerous machine learning (ML) models have been developed to detect APS, including logistic regression, nearest neighbor methods, random forest, support vector machines, and neural networks [11]. Deep learning approaches with multiple hidden layers, which allow the model to learn complex representations of the input variables, have recently obtained high accuracy performance in detecting APS [12, 13]. Enhancing the interpretability of the ML models developed to detect APS in everyday life is critical to increasing trustworthiness of the models, particularly in medical applications such as AID in people with T1D. Accurate detection of APS necessitates a multivariable approach in which multiple signals from wearable devices are analyzed to deduce the presence of APS. This has led to the development of multivariable AID systems that incorporate numerous physiological measurements from wearable devices such as wristband activity trackers. Multivariable AID systems use the additional physiological signals for feedforward control, and are able to assess the glycemic effects of disturbances such as APS before its effects are observed in the blood glucose concentration measurements. This feedforward mechanism in combination with feedback is more proactive, and represents a substantial improvement over AID systems that react to the blood glucose concentration measurements and only employ feedback control. Multivariable AID systems can use the real-time physiological measurements from wearable devices to detect APS and assess its effects on the predicted future glucose trajectory. This allows the multivariable AID system to accurately predict the future blood glucose levels, which enables timely insulin adjustments to maintain blood glucose levels within the desired range and prevent hyperglycemia. This proactive approach to handling APS necessitates accurate, reliable, and trustworthy artificial intelligence techniques that can detect the presence of APS. Previous research works have studied the detection of APS using the eXtreme Gradient Boosting (XGBoost) algorithm and recurrent neural networks with long short-term memory [12]. Class imbalances were handled using upsampling, downsampling, weighted training, and an adaptive synthetic sampling approach. The ML models were trained with features generated from convolutional kernels and features were retained for training based on the sequential forward selection algorithm [13]. These ML algorithms are typically black-box models evaluated based on accuracy.