Machine Unlearning in Digital Healthcare: Addressing Technical and Ethical Challenges

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.

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Publication Details

Journal
Proceedings of the AAAI Symposium Series
Published
2024-11-08
DOI
https://doi.org/10.1609/aaaiss.v4i1.31809
Citations
3
Primary Topic
COVID-19 diagnosis using AI
Type
article
Field-Weighted Citation Impact
1.21

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article

Machine Unlearning in Digital Healthcare: Addressing Technical and Ethical Challenges

Mengjun Xie, Shahnewaz Karim Sakib
3 citations
Proceedings of the AAAI Symposium Series
COVID-19 diagnosis using AI
1.21
article

Machine Unlearning in Digital Healthcare: Addressing Technical and Ethical Challenges

Mengjun Xie, Shahnewaz Karim Sakib
article en
3 citations

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.

Proceedings of the AAAI Symposium SeriesVol. 4(1)
University of Tennessee at Chattanooga (US)
National Science Foundation
Decent work and economic growth
Openalex Percentile: Top 19%
COVID-19 diagnosis using AI
1.21
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Machine Unlearning in Digital Healthcare: Addressing Technical and Ethical Challenges — Mengjun Xie, Shahnewaz Karim Sakib · Proceedings of the AAAI Symposium Series (2024) | TGRS Research Map | TGRS