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Considerations for IRB Review: Artificial Intelligence & Machine Learning

While artificial intelligence and machine learning are not new concepts in science, in the last decade the clinical research community has seen tremendous growth in these areas. Researchers are developing new applications at a rapid pace, and while it is exciting, it is also maybe a bit scary at the same time.

But what do “artificial intelligence” and “machine learning” really mean?

Technically, “if/then” is an algorithm. Statistical tests are also algorithms, but they are different than AI algorithms, which are a continual process of providing new inputs.

AI algorithms take both inputs and outputs simultaneously in order to “learn” the data and produce appropriate outputs when given new/unfamiliar inputs. AI algorithms typically consist of a collection of algorithms. In machine learning, AI algorithms are “fed” data and are asked to process it. An AI system can make assumptions, test, and learn autonomously.

Those AI “assumptions” have to be managed or confined; in other words, there must be some human control over the assumptions. In 2017, Facebook Artificial Intelligence Research (FAIR) trained chatbots on a collection of text conversations in English between humans playing a simple trading game involving balls, hats, and books. When programmed to experiment with the English language and tasked with optimizing trades, the chatbots seemed to evolve a reworked version of English to better solve their task.

To quote recent thoughts on ethics and machine learning, “Academia and wider society have laid down ethical principles as a way to ward off a repeat of bitter historical events, but it certainly seems that these will be eroded by uncertainties about consent, harm, and even what constitutes a human subject.”  Such statements suggest a need for more specific guidelines in this space, which are currently scarce.

The Department of Defense (DoD) has developed guidance suggesting research involving AI should be designed to be:

With that guidance in mind, institutional review boards (IRBs) may want to include the following additional considerations when reviewing research involving AI and machine learning:

Recently, the DoD changed the provision for scientific review of research to include minimal risk research. It could very well be because of the increasing volume of AI research, as more research into AI is developed. While this idea seems scary, IRBs are well equipped to handle ethical review and oversight of artificial intelligence research: after all, it starts with human data.

Learn more about IRB oversight of innovative research modalities in this podcast:


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