Promoting the relevance of academic research in the design, implementation, and evaluation of financial market regulation. Through events, analysis, and commentary, the program on financial markets regulation aims to elevate the role evidence-based decision making in the policy development process. Bringing the rigor of peer-reviewed research to decision-makers can mitigate the bias and conflicts that underlie many proposed regulatory actions, and lead to more balanced consideration of competing interests and perspectives among financial market participants. To achieve this, each initiative is focused on raising awareness of where evidence in support of financial market policy is needed, promoting regulator engagement with academic experts, and creating incentives for academics to apply their expertise to policy issues by measuring the relevance of their contributions to regulatory outcomes.Before I dive in, I think it makes sense to start with some level setting remarks about the definition of machine learning. I gave my first machine learning talk in 2015. At that time, Wikipedia defined the term as “the study of algorithms that could learn from data.” By 2018 their posted definition was “a field in computer science that gives computers the ability to learn without being explicitly programmed.” As of this week, Wikipedia says it is the “study of algorithms and statistical models that computer systems use to perform a specific task without using explicit instructions, relying on patterns and inference instead.”
So, when we talk about regulating the use machine learning, we need to first recognize that it is a bit of an elusive concept. The semantics have changed over time, and I suspect they will continue to do so. This can be a challenge to a regulator seeking to draw bright lines around practices that use it.Salem Cente
