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Machine Unlearning in Digital Healthcare: Addressing Technical and Ethical Challenges

Year: 2024

Type: conference-paper

Abstract: The ``Right to be Forgotten," as outlined in regulatory frameworks such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA), allows individuals to request the deletion of their personal data from deployed machine learning models. This provision ensures that individuals can maintain control over their personal information. In the digital health era, this right has become a critical concern for both patients and healthcare providers. To facilitate the effective removal of personal data from machine learning models, the concept of ``machine unlearning" has been introduced. This paper highlights the technical and ethical challenges associated with machine unlearning in digital healthcare. By examining current unlearning methodologies and their limitations, we propose a roadmap for future research and development in this field.

Source: Proceedings of the AAAI Symposium Series

DOI: 10.1609/aaaiss.v4i1.31809

Authors: Shahnewaz Karim Sakib, Mengjun Xie

Institution: University of Tennessee at Chattanooga

Language: English

Topic: Privacy-Preserving Technologies in Data

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: diamond

Funder: National Science Foundation

Awards: Collaborative Research: SFS Program: Strengthening the National Cyber Security Workforce, Collaborative Research: CyberCorps Scholarship for Service (Renewal): Strengthening the National Cybersecurity Workforce with Integrated Learning of AI/ML and Cybersecurity