Feedback and Blacker Boxes

Thoughts on the topic of complexity and understanding the operational specifics of what we have done.

The Blacker the Box  By Michael Kaminsky

There has been a lot of discussion in the data science community about the use of black-box models, and there is lots of really fascinating ongoing research into methods, algorithms, and tools to help data scientists better introspect their models. While those discussions and that research are important, in this post I discuss the macro-framework I use for evaluating how black the box can be for a prediction product.

In this post I do not get into the weeds of complexity penalization algorithms or even how to weigh the tech debt associated with additional complexity. Instead, I want to take a step back and discuss how I think about “prediction” problems at a more macro level, and how I value accuracy and complexity for different types of problems.

The thesis of this post is:

The faster the feedback on prediction accuracy, the blacker the box can be. The slower the feedback, the more your models should be explicit and formal.

In this post I talk through some examples of fast feedback problems and what makes them more amenable to black-box prediction algorithms (provided you have the proper infrastructure) as well as slower feedback problems and how one might approach predictions in those situations.

Fast Feedback

The machine learning community spends the bulk of its time working on and talking about fast feedback problems. Problems with fast feedback are defined by 1) the ability to quickly evaluate the correctness of a prediction1 and 2) the ability to play the game near infinite amounts of time2. Some examples of fast feedback problems are:

Chess: it is easy to verify which player has won or lost. Feedback takes only as long as the length of the game.
Conversion for an Ad Placement: Feedback to Google or Facebook on whether you clicked a given advertisement, and whether you subsequently converted  3 is nearly instantaneous.
Movie Recommendations: For a given list of potential movies to watch, Netflix gets near instantaneous feedback when you do or do not watch some of the content they have elevated for you. .... "