  This study explores the possibility of facilitating algorithmic
decision-making by combining interpretable artificial intelligence (XAI)
techniques with sensor data, with the aim of providing researchers and
clinicians with personalized analyses of cannabis intoxication behavior. SHAP
analyzes the importance and quantifies the impact of specific factors such as
environmental noise or heart rate, enabling clinicians to pinpoint influential
behaviors and environmental conditions. SkopeRules simplify the understanding
of cannabis use for a specific activity or environmental use. Decision trees
provide a clear visualization of how factors interact to influence cannabis
consumption. Counterfactual models help identify key changes in behaviors or
conditions that may alter cannabis use outcomes, to guide effective
individualized intervention strategies. This multidimensional analytical
approach not only unveils changes in behavioral and physiological states after
cannabis use, such as frequent fluctuations in activity states, nontraditional
sleep patterns, and specific use habits at different times and places, but also
highlights the significance of individual differences in responses to cannabis
use. These insights carry profound implications for clinicians seeking to gain
a deeper understanding of the diverse needs of their patients and for tailoring
precisely targeted intervention strategies. Furthermore, our findings highlight
the pivotal role that XAI technologies could play in enhancing the transparency
and interpretability of Clinical Decision Support Systems (CDSS), with a
particular focus on substance misuse treatment. This research significantly
contributes to ongoing initiatives aimed at advancing clinical practices that
aim to prevent and reduce cannabis-related harms to health, positioning XAI as
a supportive tool for clinicians and researchers alike.
