Dr. Kush R. Varshney: TrustworthyMachine Learning
Explore the development of machine learning systems that are not only accurate but also fair, robust, explainable, transparent, inclusive, and beneficial.
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daniweng
Rachele Hendricks-Sturrup: Policy and Ethics in Innovation: Developing and Applying Ethics and Equity Principles, Terms, and Engagement Tools in AI and Machine Learning
Rachele Hendricks-Sturrup, discussed policy and practice considerations in the trustworthy development and use of AI/ML in health research and care settings.
Dr. Michael Abramoff: Regulatory Considerations: Health Equity, AI, and Bias
Dr. Michael D. Abràmof discussed the transformative role of autonomous AI in healthcare, focusing on its application in clinical tools, eye exams for diabetes, and the regulatory landscape.
Trustworthiness
Trustworthiness, a commonly recognized antecedent to trust, can be described as the perception of probabilities, or expectation, that a trusting relationship will result in gains and/or losses from engaging in an encounter that requires trust.
Cybil Roehrenbeck: Legal and regulatory landscape of healthcare AI technologies
Cybil Roehrenbeck discussed the rapidly evolving landscape of AI regulations in clinical settings.
Apply to AIM-AHEAD Research and Fellowship opportunities in AI/ML and Health Equity
The Artificial Intelligence/Machine Learning Consortium to Advance Health Equity and Researcher Diversity (AIM-AHEAD) program seeks to increase the participation and engagement of the researchers and communities currently underrepresented in AI/ML modeling and applications through mutually beneficial partnerships. The Program launched…
Invisible Populations
A subset of the population that is not considered in healthcare clinical trials and are not considered in the data sets for new AI applications.
Glamour AI
AI that has little or no meaningful clinical value
Dr. Sudip Gupta: Algorithmic Bias and Causal Fairness in Healthcare Evidence from MIMIC database
Dr. Gupta discussed causal fairness principles to mitigate bias and promote equity in healthcare using MIMIC 3 data.
Causal Fairness in Healthcare
Causal fairness in healthcare refers to an ethical and methodological approach aimed at addressing disparities and ensuring equity in healthcare outcomes by focusing on the underlying causal relationships between interventions and health outcomes.