A potential issue with automated trading systems, that are based on Machine Learning (ML) and/or Artificial Intelligence (AI), is the difficulty of assessing the risk of a trade. An ML/AI algorithm may analyze thousands of parameters in order to come up with a trading decision and applying standard risk management practices might interfere with the algorithms by overriding the algorithm's decision.
What are some basic methodologies for applying risk management to ML/AI-based automated trading systems without hampering the decision of the underlying algorithm(s)?
An example system would be: Genetic Programming algorithm that produces trading agents. The most profitable agent in the population is used to produce a short/long signal (usually without a confidence interval).