Interacting with ML Models

The main difference between data analysis today, compared with a decade or two ago, is the way that we interact with it. Previously, the role of statistics was primarily to extend our mental models by discovering new correlations and causal rules. Today, we increasingly delegate parts of our reasoning processes to algorithmic models that live outside our mental models. In my next few posts, I plan to explore some of the issues that arise from this delegation and how ideas such as model interpretability can potentially address them. Throughout this series of posts, I will argue that while current research has barely scratched the surface of understanding the interaction between algorithmic and mental models, these issues will be much more important to the future of data analysis than the technical performance of the models themselves.