Principles and Policy Recommendations for Comprehensive Genetic Data Governance

TL;DR

This paper proposes a comprehensive framework for genetic data governance, emphasizing privacy, ethical principles, and policy recommendations to address risks like discrimination amid advancing AI technologies.

Contribution

It introduces a novel risk assessment framework based on four governance pillars and applies it to real-world cases, highlighting regulatory gaps and proposing policy solutions.

Findings

Identified critical gaps in current genetic data regulations.

Highlighted risks of genetic discrimination and privacy breaches.

Provided policy recommendations to enhance data governance.

Abstract

Genetic data collection has become ubiquitous, producing genetic information about health, ancestry, and social traits. However, unregulated use, especially amid evolving scientific understanding, poses serious privacy and discrimination risks. These risks are intensified by advancing AI, particularly multi-modal systems integrating genetic, clinical, behavioral, and environmental data. In this work, we organize the uses of genetic data along four distinct "pillars", and develop a risk assessment framework that identifies key values any governance system must preserve. In doing so, we draw on current privacy scholarship concerning contextual integrity, data relationality, and the Belmont principle. We apply the framework to four real-world case studies and identify critical gaps in existing regulatory frameworks and specific threats to privacy and personal liberties, particularly through genetic discrimination. Finally, we offer three policy recommendations for genetic data governance that safeguard individual rights in today's under-regulated ecosystem of large-scale genetic data collection and usage.