Preparing Tomorrow’s Leaders at the Intersection of
Artificial Intelligence/Machine Learning and Health Equity
AIM-AHEAD Fellowship Program in Leadership will engage a diverse group of participants from under- represented populations to actively participate in mentored didactic and experiential educational activities to convey the leadership competencies necessary to promote and achieve the strategic imperatives of AIM-AHEAD.
The AIM-AHEAD Coordinating Center invites applications for a Leadership Fellowship in Artificial Intelligence/Machine Learning (AI/ML) to Advance Health Equity and Researcher Diversity. The overarching goal of AIM-AHEAD is to increase the participation and engagement of researchers and communities currently underrepresented in AI/ML to utilize the power of AI/ML to achieve health equity through mutually beneficial partnerships. It has become clear, however, even in the early stages of this initiative, that AI/ML lacks diverse researchers and underrepresented communities of practice. The acquisition of AI/ML skills by individuals from these diverse backgrounds, that go beyond academic settings and into the community, promises to improve the science and increase the availability of relevant data. Yet technical competence alone does not guarantee the continued expansion and adoption of AI/ML among the populations of interest. It requires talented people of color and disadvantage from diverse settings sufficiently conversant in AI/ML to translate its promise for advancing health equity and who, in turn, can attract others to join in this common agenda.
This unique Leadership Fellowship program seeks to prepare tomorrow’s leaders to champion the use of AI/ML in addressing persistent health disparities in ways that sustain AIM-AHEAD beyond demonstrated scientific benefit and develop the human capital to promote continued engagement, adoption, and expansion at the level of programs, systems, and policies. Leadership Fellows will be matched with mentors based on AI/ML expertise, leadership experience, and sector.
In addition to mentorship, the fellows will have access to a vast number of resources, including opportunities to:
- Receive a fellowship award of $50,000. The fellowship award will be paid to the fellow as nine equal payments at the end of each month for the nine months of the program.
- Access resources across all Consortium Cores.
- Take targeted trainings and courses specific to the AI/ML and Health Equity education.
- Establish meaningful working relationships with mentors who have expertise in AI/ML, Health Equity and community engagement.
- Participate in workshops and seminars in leadership principles, strategies, case examples and lessons learned.
- Participate in bimonthly professional development and networking activities.
- Working together with the mentors to develop a study question and work with a community organization on a collaborative project.
- As existing opportunities permit, fellows may either independently pursue the aforementioned study question or can join a study already in progress consistent with their interests. The Fellowship Program will identify the latter opportunities and assist fellows in joining such efforts.
- Engage with other Fellows in the current and future cohorts through AIM-AHEAD Connect.
A detailed overview of the Fellowship Program, activities, expectations, and resources can be found at: https://aim-ahead.net/Fellows/LeadershipFellows
Fellowships are expected to begin by: September 12, 2022 for a duration of nine (9) months.
- Applicants must be:
- Early-career investigators, junior faculty, healthcare providers, administrators, policy-makers, or general community members with interest in AI/ML and Health Equity.
Not eligible: Graduate students, undergraduate students, medical students and postdocs, and other such trainees are not eligible.
- U.S. Citizens, Permanent Residents, or Non-Citizen U.S. Nationals.
- Affiliated with one of the following entities:
- Institutions of higher education such as, but not limited to Historically Black Colleges/Universities, Hispanic Serving Institutions, Tribal Colleges/Universities, and other designated minority-serving institutional equivalents;
- Healthcare organizations, e.g., healthcare delivery systems, insurers/payers, or regulatory bodies;
- Business enterprises, e.g., pharmaceutical and biotechnology industries;
- Local or state health departments;
- Independent research or policy institutes;
- Community organizations, e.g., local, regional, and national healthcare advocates, or
- Faith-based institutions.
- The goal of the AIM-AHEAD Coordinating Center is to diversify the research workforce in AI/ML and Health Equity. Therefore, consistent with the NIH’s Interest in Diversity (NOT-OD-20-031: Notice of NIH's Interest in Diversity), the following individuals are highly encouraged to apply for the Fellowship support:
- Individuals from health disparity populations that have been shown by the National Science Foundation to be underrepresented in health-related sciences on a national basis (see data at http://www.nsf.gov/statistics/showpub.cfm?TopID=2&SubID=27 and the report Women, Minorities, and Persons with Disabilities in Science and Engineering). The following racial and ethnic groups have been shown to be underrepresented in biomedical research: Blacks or African Americans, Hispanics or Latinos, American Indians or Alaska Natives, Native Hawaiians and other Pacific Islanders. In addition, it is recognized that underrepresentation can vary from setting to setting; individuals from racial or ethnic groups that can be demonstrated convincingly to be underrepresented by the grantee institution should be encouraged to participate in NIH programs to enhance diversity. For
more information on racial and ethnic categories and definitions, see the OMB Revisions to the Standards for Classification of Federal Data on Race and Ethnicity (https://www.govinfo.gov/content/pkg/FR-1997-10-30/html/97-28653.htm).
- Individuals with disabilities, who are defined as those with a physical or mental impairment that substantially limits one or more major life activities, as described in the Americans with Disabilities Act of 1990, as amended. See NSF data at: https://www.nsf.gov/statistics/2017/nsf17310/static/data/tab7-5.pdf
- Individuals from disadvantaged backgrounds, defined as those who meet two or more of the following criteria:
- Were or currently are homeless, as defined by the McKinney-Vento Homeless Assistance Act (Definition: https://nche.ed.gov/mckinney-vento/);
- Were or currently are in the foster care system, as defined by the Administration for Children and Families (Definition: https://www.acf.hhs.gov/cb/focus-areas/foster-care);
- Were eligible for the Federal Free and Reduced Lunch Program for two or more years (Definition: https://www.fns.usda.gov/school-meals/income-eligibility- guidelines);
- Have/had no parents or legal guardians who completed a bachelor’s degree (see https://nces.ed.gov/pubs2018/2018009.pdf);
- Were or currently are eligible for Federal Pell grants (Definition: https://www2.ed.gov/programs/fpg/eligibility.html);
- Received support from the Special Supplemental Nutrition Program for Women, Infants and Children (WIC) as a parent or child (Definition: https://www.fns.usda.gov/wic/wic-eligibility-requirements).
- Grew up in one of the following areas: a) a U.S. rural area, as designated by the Health Resources and Services Administration (HRSA) Rural Health Grants Eligibility Analyzer (https://data.hrsa.gov/tools/rural-health), or b) a Centers for Medicare and Medicaid Services-designated Low-Income and Health Professional Shortage Areas (qualifying zip codes are included in the file). Only one of the two possibilities in #vii can be used as a criterion for the disadvantaged background definition.
Students from low socioeconomic (SES) status backgrounds have been shown to obtain bachelor’s and advanced degrees at significantly lower rates than students from middle and high SES groups (see https://nces.ed.gov/programs/coe/indicator_tva.asp), and are subsequently less likely to be represented in biomedical research. For background see Department of Education data at:
- Literature shows that women from the above backgrounds (categories A and B) face particular challenges at the graduate level and beyond in scientific fields. (See, e.g., From the NIH: A Systems Approach to Increasing the Diversity of Biomedical Research Workforce https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5008902/). Women are known to be underrepresented in doctorate-granting research institutions at senior faculty levels in most biomedical-relevant disciplines, and may also be underrepresented at other faculty levels in some scientific disciplines (See data from the National Science Foundation National Center for Science and Engineering Statistics: Women, Minorities, and Persons with Disabilities in Science and Engineering, special report available at https://www.nsf.gov/statistics/2017/nsf17310/, especially Table 9-23, describing science, engineering, and health doctorate holders employed in universities and 4-year colleges, by broad occupation, sex, years since doctorate, and faculty rank).
Please use single space, Arial, 11pt with 0.5” margins.
- Profile Information (to be entered on AIM-AHEAD Connect)
- Provide your name, organization, department (if applicable), position title, areas of interest/expertise, email address, and your profile web page (optional)
- Gender, race/ethnicity (optional)
- Biosketch (5 pages limit) in NIH (https://grants.nih.gov/grants/forms/biosketch.htm) or other format (Curriculum vitae of 5 pages is acceptable)
- Letter of interest (limit 3 pages, not including references) addressing the following questions:
- Why are you interested in becoming a champion of AI/ML for your community? How would these skills fit with your current interests and future career goals?
- What is your level of familiarity with (and/or interest in) Social Determinants of Health, Health Equity, Health Disparities, program management, organizational innovation, AI/ML analysis, EHR data, biomedical science, public health background and cloud-based computation (if any)?
- What question most interests you in regard to AI/ML research that may speak to the health disparities that afflict the population(s) with which you work?
- With respect to this question, are you currently affiliated with a particular stakeholder organization or community that shares this interest and which might join you in addressing it?
- How do you envision applying the skills, knowledge, and lessons learned through this fellowship to promoting the appropriate, equitable use of AI/ML to reduce health disparities?
- Two letters of reference:
- Signed letter from home agency/institution approving commitment to half-time of protected effort to participate for the full 9-months of your participation in the leadership program
- Signed letter from an appropriate key stakeholder setting or program approving engagement with it in applying AI/ML methods to data within its control to host the 9- months of your participation in the leadership program. Please note: The home agency/institution and key stakeholder may be the same organization for some applicants.
Fellows will be expected to participate in all components of the Leadership Program, which include:
- Highly focused, basic instruction in AI/ML methods with an emphasis on the benefit for diverse individuals, programs, agencies, and communities. This element will draw upon select offerings available through the Data Science Training Core. Health equity and health disparities should be interwoven throughout all training components.
- Affiliation with a diverse stakeholder setting, e.g., a healthcare agency, minority-serving institution, business enterprise, or community organization, that is engaged or has interest in applying AI/ML methods to data consistent with the scientific objectives of AIM-AHEAD.
- Continuous mentorship by an experienced, skilled individual who can guide the fellow in exploring the potential benefits and harms in applying AI/ML methods to address health disparities, and leadership skills using the servant leadership model to assist them in navigating the organizational cultures and climates to strengthen their communities.
- Conducting or participating in a study that involves the use of AI/ML to address health disparities relevant to a particular population or health setting. Mentors will assist fellows in finalizing the design and pursuit of new questions. Alternatively, Fellowship Program staff will assist fellows in joining a study already in progress consistent with their interests.
- Fellows are expected to provide updates on a regular basis for Progress Reports to the NIH and a final report of their project.
- Participation in bimonthly virtual meetings of other fellows that support:
- Instruction in leadership principles, strategies, and tactics embedded within case examples.
- Opportunities to share individual progress, challenges, and emerging solutions with an emphasis on lessons learned that illustrate these leadership principles, strategies, and tactics.
- Pursuit of a study question in AI/ML and Health Equity through a collaborative project with a community stakeholder. As noted above, this effort may entail either a new line of inquiry or joining a project in progress.
- Participation in an AIM-AHEAD Work Group of the fellow’s choice as a means of acquiring direct exposure to priority-setting, group decision-making, and implementation processes in AI/ML.
- Participation in a select number of meetings of the AIM-AHEAD Coordinating Center and Leadership/Administrative Core for first-hand exposure to managerial processes and governance structures underlying coordinated solutions to the challenges of leading a nationwide, multi-year, high resource initiative.
- Participation in the AIM-AHEAD annual meeting or another sponsored AIM-AHEAD meeting for the Leadership program.
- Participation in a proactive evaluation of program process and outcomes, the results of which will inform the revision of subsequent offerings.
Leaders Alumni Program: Fellows will have the opportunity to participate in an AIM-AHEAD Leaders Alumni Program for sustained exposure to AI/ML health equity, and leadership skills to maintain human capital developed in the fellowship program.
Each awarded Fellow will receive mentorship from an experienced, skilled individual who can guide the fellow in applying AI/ML methods to address health disparities, and leadership skills using the servant leadership model to assist them in navigating the organizational cultures and climates to strengthen their communities. Applicants are not expected to identify a mentor at time of application. The Fellowship Program will -- based upon such factors as health disparities interests, population of concern, AI/ML expertise, and ongoing work – identify, introduce, and secure the agreement of appropriate mentors to support the fellows’ successful participation in the program. All costs related to mentors’ participation are absorbed by the Fellowship Program and are not included in the application.
After acceptance into the program, Fellows will be matched with Mentors who have AI/ML and health equity expertise in areas of research interest to the Fellows. Mentors will be drawn from existing AIM-AHEAD members (and/or additional mentors brought by the Fellows) as appropriate, but will also be recruited from a wide array of educational institutions, industry, and healthcare organizations. Mentors will have demonstrated experience in leadership in health equity and/or AI/ML and drawn from various organizations, institutions and healthcare sectors. Mentors will meet with the fellows virtually on a weekly basis and in person at the AIM-AHEAD annual meeting or another sponsored meeting.
Proposals will be reviewed using the following criteria and reviewer questions:
- Applicant evaluation:
- Does the applicant describe their leadership and research interest in AI/ML to address health disparities?
- To what extent does the applicant have the potential to serve as a community leader in AI/ML?
- To what extent does the applicant demonstrate a familiarity with key concepts, such as social determinants of health and machine learning, that will be addressed during the Fellowship?
- Has the applicant identified affiliation with a stakeholder organization for which they will conduct their mentored research project during the Fellowship?
- To what extent has the applicant thoughtfully described how s/he will apply the skills, knowledge, and lessons learned through this fellowship to promote the appropriate, equitable use of AI/ML to reduce health disparities?
- To what extent does the applicant’s goals and expectations align with the AIM-AHEAD program goals?
- Programmatic Factors:
Consideration will be given to programmatic factors such as:
- Overall cohort diversity in line with AIM-AHEAD program goals.
- Overall diversity of stakeholder settings within/with whom fellows work.
- Geographical distribution of fellows across the AIM-AHEAD hubs.
Notification: Applicants should expect to be notified of their award status by August 26, 2022.
Click here to register as a “mentee/learner” on AIM-AHEAD Connect (our Community Building Platform)
Please note both steps must be completed for consideration.
Click here to submit a fellowship application for review using InfoReady platform
All applications must be received by Monday August 8, 2022 11:59PM Eastern Time.
Watch the Fellowship in Leadership Application Preparation Workshop Recording
Director: Roland J. Thorpe, Jr., PhD
Co-Directors: Spero M. Manson, PhD, and Robert Mallet, PhD
*NOA – Notice of Award
|Application Submission Deadline