In most machine learning discussions I have with people, I find that the notion of error risk is new to them. Here’s the basic idea: you have a trained machine learning model that’s processing incoming data, and it naturally has an error rate. Let’s say the error rate is 5%, or conversely, the model is correct about 95% of the time. Error risk, then, is the set of possible negative consequences from incorrect predictions or decisions.
In an extreme example, the error risk for a 747’s autopilot system is perilously high. For a model predicting user shopping behavior on an e-commerce site, the risk is rather low – maybe it recommends the wrong product once or twice, but nobody gets hurt.
The depth of the model’s integration and the speed at which it makes decisions are both correlated with the amount of error risk. If the program in question is running some analytics off the side, and merely supplying supplementary information to some human decision-maker, the risk is almost zero. However, if the program is itself making decisions, such as how much to bank right in a 45mph crosswind or how much of a certain inventory to order from a supplier, the risk increases substantially.
I’ve taken to quantifying error risk by asking the following questions:
- Is the program or system making autonomous decisions? If yes, what happens when the wrong decision is made?
- If it is making decisions, what is the cycle time / how quickly are those decisions being made?
- If it is not making decisions, is the information it’s providing critical or supplementary? (Critical information could be things like cancer diagnostics, whereas supplementary information could be providing simple reports to a digital marketing team.)
Other questions come up in these situations, but the above are the most important.
Optimal use of machine learning in applications means gaining maximal benefit at minimum risk wherever possible. To get as close as possible to “pure upside” in implementing machine learning, what’s required is some strategic thinking around where the opportunities lie and what the error tolerance might be in those applications. Even state-of-the-art machine learning systems have intrinsic error. Therefore error risk must always be accounted for, even if the error size is tiny.
My ideas about optimal implementation of machine learning borrow heavily from ideas in portfolio optimization, especially the efficient frontier. This is the “sweet spot” in the tradeoff between rewards and risks. As machine learning makes its way into more applications, it’s worth taking the time to consider both the upside and the downside. Measures of optimality can only help to make more informed decisions about how to apply the latest technology.