Integrating Machine Learning with Product Design

Presumably the reason you’re doing this work is because you hope it will be used for something. Otherwise, you’re just wasting your time. So, let’s start with the assumption that your work will end up somewhere. Now, as you are collecting your data and developing your model, you are making lots of decisions. What level of aggregation will you store your data at? What loss function should you use? What validation and training sets should you use? Should you focus on simplicity of implementation, speed of inference, or accuracy of the model? How will your model handle out-of-domain data items? Can it be fine-tuned, or must it be retrained from scratch over time?

These are not just algorithm questions. They are data product design questions. But the product managers, executives, judges, journalists, doctors… whoever ends up developing and using the system of which your model is a part will not be well-placed to understand the decisions that you made, let alone change them.

For instance, two studies found that Amazon’s facial recognition software produced inaccurate and racially biased results. Amazon claimed that the researchers should have changed the default parameters, without explaining how this would have changed the biased results. Furthermore, it turned out that Amazon was not instructing police departments that used its software to do this either. There was, presumably, a big distance between the researchers that developed these algorithms and the Amazon documentation staff that wrote the guidelines provided to the police. A lack of tight integration led to serious problems for society at large, the police, and Amazon themselves. It turned out that their system erroneously matched 28 members of congress to criminal mugshots! (And the Congresspeople wrongly matched to criminal mugshots were disproportionately people of color, as seen in <>.)

Data scientists need to be part of a cross-disciplinary team. And researchers need to work closely with the kinds of people who will end up using their research. Better still is if the domain experts themselves have learned enough to be able to train and debug some models themselves—hopefully there are a few of you reading this book right now!

The modern workplace is a very specialized place. Everybody tends to have well-defined jobs to perform. Especially in large companies, it can be hard to know what all the pieces of the puzzle are. Sometimes companies even intentionally obscure the overall project goals that are being worked on, if they know that their employees are not going to like the answers. This is sometimes done by compartmentalising pieces as much as possible.

In other words, we’re not saying that any of this is easy. It’s hard. It’s really hard. We all have to do our best. And we have often seen that the people who do get involved in the higher-level context of these projects, and attempt to develop cross-disciplinary capabilities and teams, become some of the most important and well rewarded members of their organizations. It’s the kind of work that tends to be highly appreciated by senior executives, even if it is sometimes considered rather uncomfortable by middle management.