Ethics, Bias, and Transparency for People and Machines

About Ethics, Bias, and Transparency for People and Machines

Artificial intelligence and machine learning (AI/ML) are a collection of data-driven technologies with the potential to significantly advance scientific discovery in biomedical and behavioral research. Researchers employing these technologies must take steps to minimize the harms that could result from their research, including but not limited to addressing (1) biases in datasets, algorithms, and applications; (2) issues related to identifiability and privacy; (3) impacts on disadvantaged or marginalized groups; (4) health disparities; and (5) unintended, adverse social, individual, and community consequences of research and development. Some of the inherent characteristics of AI/ML, as well as its relative newness in the biomedical and behavioral sciences, have made it difficult for researchers to apply ethical principles in the development and use of AI/ML, particularly for basic research. 

To address these issues, National Institutes of Health (NIH) Office of Data Science Strategy (ODSS) announced “Administrative Supplements for Advancing the Ethical Development and Use of AI/ML in Biomedical and Behavioral Sciences” on February 3, 2022. The goal of this notice was to make the data generated through NIH-funded research AI/ML-ready and shared through repositories, knowledgebases, or other data sharing resources.

Meetings and Reports

Closed Funding Opportunities: 

Twenty-two awards were made in 2022 to principal investigators at 33 different institutions across the country. Awardee projects and their descriptions are available below.

NOT-OD-22-065 Recipients
Principal InvestigatorInstitutionProject TitleNIH IC
Bui, AlexUniversity of California Los Angeles

PREMIERE: A PREdictive Model Index and Exchange REpository

Disis, Mary LUniversity of Washington

Developing Community-Responsive mHealth and AI/ML: Understanding Perspectives of Hispanic Community Members in Washington State

Do, Richard Kinh GianSloan-Kettering Inst Can Research

Development and Validation of Prognostic Radiomic Markers of Response and Recurrence for Patients with Colorectal Liver Metastases

Federman, Alex DIcahn School of Medicine at Mount Sinai

Natural Language Processing and Automated Speech Recognition to Identify Older Adults with Cognitive Impairment Supplement

Finkbeiner, Steven MJ. David Gladstone Institutes

Cell and Network Disruptions and Associated Pathogenenesis in Tauopathy and Down Syndrome

Goldstein, Benjamin AlanDuke University

Predictive Analytics in Hemodialysis: Enabling Precision Care for Patient with ESKD

Herrington, John DavidChildren's Hospital of Philadelphia

Ethical Perspectives Towards Using Smart Contracts for Patient Consent and Data Protection of Digital Phenotype Data in Machine Learning Environments

Holder, Andre LEmory University

Characterizing patients at risk for sepsis through Big Data (Supplement)

Jha, Abhinav KWashington University

A framework to quantify and incorporate uncertainty for ethical application of AI-based quantitative imaging in clinical decision making

Jiang, XiaoqianUniversity Of Texas Health Science Center Houston

Finding combinatorial drug repositioning therapy for Alzheimers disease and related dementias

Kamaleswaran, RishikesanEmory University

EQuitable, Uniform and Intelligent Time-based conformal Inference (EQUITI) Framework

Langlotz, Curtis PStanford University

Population-level Pulmonary Embolism Outcome Prediction with Imaging and Clinical Data: A Multi-Center Study

Naidech, Andrew MNorthwestern University at Chicago

Hemostasis, Hematoma Expansion, and Outcomes After Intracerebral Hemorrhage

Odero-Marah, ValerieMorgan State University

RCMI@Morgan: Center for Urban Health Disparities Research and Innovation

Ohno-Machado, LucilaUniversity of California, San Diego

Genetic & Social Determinants of Health: Center for Admixture Science and Technology

Olatosi, BankoleUniversity of South Carolina at Columbia

An ethical framework-guided metric tool for assessing bias in EHR-based Big Data studies

Platt, Jodyn ElizabethUniversity of Michigan at Ann Arbor

Public trust of artificial intelligence in the precision CDS health ecosystem - Administrative Supplement

Sabatello, MayaColumbia University Health Sciences

Blind/Disability and Intersectional Biases in E-Health Records (EHRs) of Diabetes Patients: Building a Dialogue on Equity of AI/ML Models in Clinical Care

Sjoding, Michael WilliamUniversity of Michigan at Ann Arbor

Human-AI Collaborations to Improve Accuracy and Mitigate Bias in Acute Dyspnea Diagnosis

Wolf, Risa MichelleJohns Hopkins University

Autonomous AI to mitigate disparities for diabetic retinopathy screening in youth during and after COVID-19

Wun, TheodoreUniversity of California at Davis

UC Davis Clinical and Translational Science Center

Zeng, QingGeorge Washington University

Use Explainable AI to Improve the Trust of and Detect the Bias of AI Models

Zhi, DeguiUniversity Of Texas Health Science Center Houston

Genetics of deep-learning-derived neuroimaging endophenotypes for Alzheimer's Disease (Parent grant)


This page last reviewed on February 7, 2024