rstudio::conf(2022)

Machine learning models, applied in the real world, can have unanticipated, harmful side effects. Recommended counter-measures include structured documentation of models ("Model Cards for Model Reporting") and training data used ("Data Sheets for Datasets").

In this talk, I'd like to propose a similar, multi-dimensional approach to analyzing the "solution" as a whole - "solution" as in "tech solutionism", the common term for technical "fixes" that have unintended, harmful consequences.

The idea here is that, by asking WHAT a solution is doing, WHO is providing it and WHY, as well as WHERE and HOW it will be used, we should be able to systematically assess whether we are, in fact, confronted with an instance of tech solutionism.