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To Bot or Not to Bot?
Participants
Summary
The disclosure of personal information related to well-being and mental health requires a high degree of trust between individuals and healthcare providers. However, traditional methods for conducting initial mental health assessments are often time-consuming and resource-intensive, creating challenges due to growing demand, limited funding, and workforce shortages. This research explores the potential of conversational agents (chatbots and AI-driven assistants) to collect sensitive mental health data in a way that is both efficient and trustworthy. The study examines factors influencing trust in AI-driven assessments, including privacy, empathy, transparency, and accuracy, while evaluating how these technologies can reduce the workload of trained professionals. By analyzing user experiences, ethical considerations, and data security measures, this project aims to determine whether conversational agents can serve as a reliable first point of contact in mental health care, ultimately improving accessibility, efficiency, and patient outcomes.
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Publications
[1] Taylor, D., Melvin, C., Aung, M. H., & Asif, R. (2024). To Bot or Not to Bot? Analysing Mental Health Data Disclosures. 97-115. Paper presented at International Conference on Human-Computer Interaction, Washington DC, Washington, United States. https://doi.org/10.1007/978-3-031-61379-1_7
Secure and Ethical Approaches to Employee Wellness Measurement
Participants
Summary
Employee wellness programs are increasingly leveraging digital tools, wearable devices, and AI-driven analytics to assess physical, mental, and emotional well-being. However, the collection and processing of sensitive health and behavioural data raise significant concerns regarding data security, privacy, and ethical usage. This research explores secure and privacy-preserving approaches to employee wellness measurement, ensuring that workplace well-being initiatives do not compromise personal data protection. The study examines the role of blockchain for secure data storage, federated learning for decentralized AI-driven analysis, and differential privacy techniques to prevent unauthorized access. Additionally, it evaluates compliance with GDPR, HIPAA, and other data protection regulations while proposing a governance framework for ethical data handling. By integrating cybersecurity best practices and privacy-enhancing technologies, this research aims to develop a trustworthy, data-driven approach to employee wellness that benefits both organizations and their workforce without compromising individual rights.
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[1] Buckley, O., Hodges, D., Windle, J., & Earl, S. (2022). CLICKA: Collecting and leveraging identity cues with keystroke dynamics. Computers & Security, 120, Article 102780. https://doi.org/10.1016/j.cose.2022.102780
[2] Earl, S., Campbell, J., & Buckley, O. (2021). Identifying Soft Biometric Features from a Combination of Keystroke and Mouse Dynamics. In M. Zallio, C. Raymundo Ibañez, & J. H. Hernandez (Eds.), Advances in Human Factors in Robots, Unmanned Systems and Cybersecurity - Proceedings of the AHFE 2021 (pp. 184-190). (Lecture Notes in Networks and Systems; Vol. 268). Springer. https://doi.org/10.1007/978-3-030-79997-7_23