Privacy, ethics, transparency, and accountability in AI systems for wearable devices.

Area of Science:

• Health Data Science
• Artificial Intelligence
• Wearable Sensor Technology

Background:

• AI and ML integration in wearables advances health data science, enabling continuous monitoring and personalized interventions.
• Rapid technological progress introduces significant ethical and regulatory challenges, including data privacy, algorithmic bias, and lack of transparency.
• Existing frameworks struggle to address risks from unregulated data aggregation and biased AI models in healthcare.

Purpose of the Study:

• To systematically examine ethical and regulatory challenges in AI-powered wearable health technologies.
• To identify risks associated with data privacy, algorithmic bias, and opaque decision-making.
• To propose a methodological framework for responsible AI development in wearable healthcare.

Main Methods:

• Systematic examination of ethical and regulatory challenges.
• Analysis of current privacy frameworks and empirical assessment of public datasets.
• Development of a data-driven methodological framework incorporating explainable AI and bias mitigation.

Main Results:

• Significant disparities in AI model performance across demographic groups were identified.
• Vulnerabilities in technical design and ethical governance of AI health applications were exposed.
• A methodological framework was introduced to embed transparency, accountability, and regulatory alignment.

Conclusions:

• The proposed framework operationalizes ethical principles for AI development in wearable healthcare.
• It aligns with legal standards like GDPR and the EU AI Act, promoting responsible innovation.
• Advocates for a balanced regulatory approach to ensure fair, secure, and trustworthy AI-driven health monitoring.