The three largest concerns around the ethics of AI/ML in artificial intelligence human subject research are explainability, identifiability, and algorithmic bias. It is imperative that IRBs understand the AI/ML’s explainability in order to fulfill their responsibility of adequately assessing the risk-benefit ratio in AIHSR. Read more
In the not-so-distant past, IRBs reviewing artificial intelligence and machine learning protocols were quick to give not-human subject research determinations because the application was presented as a software development project. More recently, many IRBs improperly issue exempt determinations because the application is presented as a secondary-use data project. Read more
There’s a growing trend in Social, Behavioral, and Education Research (SBER)–machine learning–in which investigators often request to obtain, through direct interaction and intervention, various sets of data on human subjects, including their physiological (i.e., data obtained from either invasive or non-invasive means) and/or biometric data (e.g., audio/visual recordings). The research as originally conceived may or may not have been considered human subjects research, but its ultimate purpose is to teach machines how to think, draw conclusions, and process information in much the same way humans do. Read more
In April, the Food and Drug Administration (FDA) issued a discussion paper, "Proposed Regulatory Framework for Modifications to Artificial Intelligence/Machine Learning (AI/ML)-Based Software as a Medical Device (SaMD).” The paper represents FDA’s response to the growing number of medical device manufacturers who are utilizing artificial intelligence and machine learning technologies to continuously improve their products. On June 3, PRIM&R submitted comments in response to the discussion paper, thanking the FDA for their consideration of the public health implications of the use of these technologies, but also cautioning that any new regulatory approach in this area must address the protection of individuals whose personal information and data are being used in the creation and ongoing testing of these technologies. Read more
