Is your AI making good decisions?

A model is only as good as the underlying training data. (A model is modeling training data.) You’re very likely creating your training data from a large pool of people you’ve never met (i.e., outsourced data labeling). But when and how do their biases affect your model?

How do you know that the nuance of the decisions are OK to model? What if the model interprets the training data in a way that’s not intuitive to you? How do you monitor AI that’s making decisions in the real world? How much should you monitor? Should we regulate companies such that they must provide some level of monitoring and centralized authority/control via statistical confidence intervals of decision outcomes? What if a superimportant model, like Google’s self-driving model, kills people? Does the Supreme Court look at the training data? Do they look at decisions made in similar scenarios? Do they make Google start from scratch on a new model? How does Google prove that their model has been improved and won’t make a bad decision again?

Brian Rieger attempts to answer some of these questions as he explores AI decision making.