Stakeholder Guidance to Anticipate and Address Ethical Challenges in Applications of Machine Learning and Artificial Intelligence in Algorithmic Medicine: a Novel Empirical Approach

PROJECT ABSTRACT The potential for artificial intelligence applications to enable more granular and pervasive measurement, prediction, and provide behavioral interventions offers immense promise in reaching the goal of precision health to maintain the overall health of populations.

When applied to devices encountered in our everyday environment, (e.g. personal computers, mobile phones, computer mice, even office furniture such as sit-stand desks), machine learning algorithms can amplify the impact of technology on health improvement by its ability to passively sense stress, and to provide just-in-time behavioral interventions based on contextual data and self-reported user feedback.

At the same time, the ethical dimensions of these innovative lines of work ? some of which entail fundamental concerns about privacy and autonomy ? require careful attention from the scientific community.

Most critically, there has been little engagement with the end-users of such technologies as a major stakeholder group who are most affected by these learning systems and tools.

This administrative supplement request is premised on the fact that the rationale for and unmet needs targeted in the scope and aims of the parent grant can be even more effectively met (i.e. not changed but enriched) by adding participants with direct exposure and personal experience of interacting with precision health technologies to the last stakeholder group in the parent grant (i.e. patients).

By extending the patient group in Aim 1 to include those directly participating in cutting-edge research at the intersection of occupational and precision health research, the Aims and Scope of the parent grant remain unchanged, while the real-world application and impact of the products from the parent grant are substantially enhanced.

Our Supplemental proposal incorporates precision health technologies involving behavioral interventions of stress management that use ML into the first Specific Aims of the parent R01.

In Supplemental Aim 1, we will use semi-structured interviews and qualitative methods to articulate ethical issues in the context of the development, refinement, and application of machine learning in behavioral interventions as part of a precision health methodology, with particular attention to occupational health contexts.

Specifically, our methodology elicits a wide range of viewpoints from participants by comparing two distinct types of machine learning applications (i.e. physical versus digital interventions), with two varying degrees of autonomy that users may exercise to accept or reject the AI-recommended interventions.

Both of these applications present novel ethical questions regarding the decision-making role of ML/AI algorithms in behavioral health research and practice.

This supplementary project leverages access to the exceptional machine learning research conducted at Stanford University, including work by NIH-funded investigators, and provides extensive, systematically collected data on ethical issues encountered and anticipated as a result of machine learning applications in precision, behavioral, and occupational health.