What are the risks of machine learning and what are the corresponding controls that can be used to mitigate those risks?
So, they do things like, performing a model validation which includes rigorous qualitative and quantitative tests, setting limitations on use, testing the sensitivity to inputs and boundary conditions, among many others.
In the traditional sense, a machine learning model is a new beast. In most cases, these models don't have an agreed upon "methodology" (i.e. neural networks) and therefore don't have an "expected" result that can be tested (especially for unsupervised learning models). So, curious to know what others are thinking?
Beyond that, there have been a number of stories in the news over the past few years that touch on various risks related to ML.
Things like: automated trading systems causing economic problems. Facial recognition systems not recognizing the faces of black employees. "Predictive policing" creating a "Minority Report" type environment. Etc.
I doubt there's any comprehensive list of "risks of machine learning" though, since there's not really a corresponding comprehensive catalog of "capabilities of machine learning".