Reidentification Risk
To remove identifying information from something (such as computer data) so that the original source cannot be known; to make something anonymous.
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Dr. Hernandez-Boussard – Ensuring Responsible AI in Healthcare: Ethical Challenges and Pathways to Equity
Dr. Hernandez-Boussard, examined ethical challenges in healthcare AI, focusing on bias, accountability, transparency, and fairness.
Dr. Ghosh – Concepts of Identity in Voice: Audio Data Processing and Anonymization
Dr. Ghosh discussed voice as a personal biometric marker that conveys identity, emotion, and intent, while also posing privacy risks in audio-driven technologies.
Anonymization
To remove identifying information from something (such as computer data) so that the original source cannot be known; to make something anonymous.
Dr. Mello – Using AI in Patient Care: Emerging Legal Issues
Dr. Leo Celi presented on the societal implications of AI, addressing concerns such as its role in accelerating climate change, reinforcing systemic inequities through data bias, and fostering monopolistic dependencies on large firms.
Digital Biomarker
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.
Hallucination
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.
Tiffany Problem
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.
Dr. Leo Celi – The ROI of Health AI: We need more roads, not Ferraris
Dr. Leo Celi presented on the societal implications of AI, addressing concerns such as its role in accelerating climate change, reinforcing systemic inequities through data bias, and fostering monopolistic dependencies on large firms.
AIM- AHEAD Training Programs Call for Applications- Deadline November 18, 2024
AIM-AHEAD Bridge2AI for Clinical Care AIM-AHEAD Traineeship in Advanced Data Analysis using the Bridge2AI AI/ML for Clinical Care NetworkThe AIM-AHEAD Bridge2AI for Clinical Care Training Program is intended to increase researcher diversity in AI/ML by leveraging Bridge2AI AI/ML for Clinical…