Ethical challenges and opportunities for integrating predictive analytics in community-based overdose prevention

Abstract

As predictive analytics become more widely integrated into local public health responses to the United States overdose epidemic, community-based substance use service providers have begun to adopt machine learning-based predictive tools to guide the allocation and delivery of overdose prevention services. While these tools hold promise for anticipating community overdose risk and enhancing the efficiency of overdose prevention resource distribution, outreach, and education efforts, their use in community settings raises substantial ethical and practical challenges. In this Viewpoint, we examine the application of predictive analytics to community-based overdose prevention through a public health ethics lens, drawing on principles of distributive justice, transparency, community participation, and implementation readiness. We outline five key ethical considerations for developers (i.e., institutional responsibility, oversimplification of complex social realities, data and algorithmic bias, community displacement in decision making, and equity trade-offs) and corresponding practical challenges for service providers. We offer five recommendations for developers, public health authorities, and frontline organizations to overcome challenges and ensure responsible, equity-driven implementation. As data-driven approaches to overdose prevention proliferate, ethical and participatory frameworks will be essential to ensure predictive tools strengthen, rather than undermine, community trust and health equity.

Keywords: Ethics; Machine learning; Overdose prevention; Predictive analytics; Public health.