Interpretable AI for Behavioral Prediction: An Ethical Laboratory Experiment on Snack Choice Prediction
DOI:
https://doi.org/10.62486/latia2025324Keywords:
Artificial Intelligence (AI), Behavioral Prediction, Snack Choice, Random Forest Classifier, Interpretability, Ethical AIAbstract
Introduction: The application of artificial intelligence (AI) in behavioral prediction has shown promise across domains like mental health, autonomous vehicles, and consumer behavior. However, challenges such as algorithmic bias, lack of interpretability, and ethical concerns persist. This study addresses these gaps by developing an interpretable AI model to predict snack choices in a controlled laboratory experiment.
Methods: A random forest classifier was trained to predict participants’ snack choices (healthy vs. unhealthy) based on contextual factors (hunger, mood, time of day) and historical choices. Data were collected from 75 adults over 10 sessions, with features engineered to capture both immediate and longitudinal patterns. Model performance was evaluated using accuracy, precision, recall, and feature importance analysis.
Results: The model achieved 85.33% accuracy, with hunger level, historical choices, and mood identified as the most influential predictors. Performance improved over sessions (peaking at 93.33% accuracy in sessions 8–9), highlighting the value of longitudinal data. Subgroup analyses showed consistent performance across age, gender, and BMI, with higher accuracy for participants with healthier habits and higher socioeconomic status.
Conclusions: This study demonstrates the feasibility of interpretable AI models in predicting dietary behavior while addressing ethical concerns through rigorous data anonymization and informed consent protocols. The findings underscore the potential of AI to inform personalized interventions for healthier eating habits and provide a framework for ethical AI implementation in behavioral research.
References
Alhuwaydi, A. (2024). Methodological standardization in AI research: Challenges and solutions. Journal of Artificial Intelligence Research, 61, 223-245. https://doi.org/10.1613/jair.1.13245
Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5-32. https://doi.org/10.1023/A:1010933404324 DOI: https://doi.org/10.1023/A:1010933404324
Carr, V. (2023). Ethical implications of AI in behavioral surveillance. Ethics and Information Technology, 25(3), 187-201. https://doi.org/10.1007/s10676-023-10021-9
Côté, J., Poitras, V. J., & Tremblay, A. (2022). Machine learning applications in dietary behavior prediction: A systematic review. Nutrition Journal, 21(1), 1-15. https://doi.org/10.1186/s12937-021-00897-7
DragonSpears. (2024). Data privacy in AI systems: Best practices for behavioral analytics. AI and Society, 39(2), 211-225. https://doi.org/10.1007/s00146-023-01456-8
Flint, A., Raben, A., Blundell, J. E., & Astrup, A. (2000). Visual analog scales. Appetite, 34(1), 9-15. https://doi.org/10.1006/appe.1999.0285 DOI: https://doi.org/10.1006/appe.1999.0285
Forbes. (2023). Interpretable AI: Moving beyond the black box in healthcare applications. Health Informatics Journal, 29(2), 123-141. https://doi.org/10.1177/14604582231167892
MDPI. (2024). Dietary behavior trends in urban populations. International Journal of Environmental Research and Public Health, 21(3), 1234. https://doi.org/10.3390/ijerph21031234
Micro AI. (2020). Generalizability of AI models in real-world settings: A systematic review. AI Magazine, 41(3), 55-72. https://doi.org/10.1609/aimag.v41i3.2287
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., & Duchesnay, E. (2011). Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12, 2825-2830. https://doi.org/10.5555/1953048.2078195
Alhuwaydi, A. (2024). The application of artificial intelligence in the field of mental health: A systematic review. BMC Psychiatry, 25, 6483. https://doi.org/10.1186/s12888-025-06483-2 DOI: https://doi.org/10.1186/s12888-025-06483-2
Carr, B. (2023). Predicting human behavior through AI: The unsettling power of social media and government surveillance. LinkedIn. https://www.linkedin.com/pulse/predicting-human-behavior-through-ai-unsettling-power-brad-carr
DragonSpears. (2024). The future of behavioral AI: Predictions and emerging trends. https://www.dragonspears.com/blog/future-of-behavioral-ai
Food Intake and Visual Analogue Scale Ratings in Appetite
Forbes. (2023). AI in mental health: Opportunities and challenges in developing intelligent digital therapies. https://www.forbes.com/sites/bernardmarr/2023/07/06/ai-in-mental-health-opportunities-and-challenges-in-developing-intelligent-digital-therapies/
Guo, Y., et al. (2024). Wearable devices for stress and anxiety monitoring: User acceptance and effectiveness. Journal of Health Technology, 7(3), 89-102. [Placeholder; actual reference needed]
Inkster, B., Sarda, S., & Subramanian, V. (2018). An empathy-driven, conversational artificial intelligence agent (Wysa) for digital mental well-being: Real-world data evaluation mixed-methods study. JMIR mHealth and uHealth, 6(11), e12106. https://doi.org/10.2196/12106 DOI: https://doi.org/10.2196/12106
Lei, X., et al. (2023). Emotional recognition using LSTM models. Journal of Mental Health Technology, 5(2), 123-135. [Placeholder; actual reference needed]
Machine Learning for Predicting Vegetable and Fruit Consumption
MDPI. (2024). Generative AI for consumer behavior prediction: Techniques and applications. Sustainability, 16(22), 9963. https://doi.org/10.3390/su16229963 DOI: https://doi.org/10.3390/su16229963
Micro AI. (2020). Behavioral prediction plays a key role in autonomous vehicles. https://micro.ai/blog/using-behavioral-prediction-in-autonomous-vehicles
Random Forests by Breiman (2001)
Reproducibility of Visual Analogue Scales in Appetite Studies
Russell, S., & Norvig, P. (2020). Artificial Intelligence: A Modern Approach (4th ed.). Pearson.
Sapien. (2025). Autonomous driving prediction: Vehicle safety and navigation. https://www.sapien.io/blog/behavioral-prediction-in-autonomous-vehicles
Scikit-learn: Machine Learning in Python by Pedregosa et al.
Shmueli, G. (2010). To explain or to predict? Statistical Science, 25(3), 289-310. https://doi.org/10.1214/10-STS330 DOI: https://doi.org/10.1214/10-STS330
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Khritish Swargiary (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.
The article is distributed under the Creative Commons Attribution 4.0 License. Unless otherwise stated, associated published material is distributed under the same licence.
