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

> **NIH NIH R01** · STANFORD UNIVERSITY · 2021 · $157,426

## Abstract

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, systematical...

## Key facts

- **NIH application ID:** 10367404
- **Project number:** 3R01TR003505-02S1
- **Recipient organization:** STANFORD UNIVERSITY
- **Principal Investigator:** Jane Paik Kim
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $157,426
- **Award type:** 3
- **Project period:** 2021-08-17 → 2024-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10367404

## Citation

> US National Institutes of Health, RePORTER application 10367404, Stakeholder Guidance to Anticipate and Address Ethical Challenges in Applications of Machine Learning and Artificial Intelligence in Algorithmic Medicine: a Novel Empirical Approach (3R01TR003505-02S1). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10367404. Licensed CC0.

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