The above is the title of a talk by computer scientist Cynthia Rudin. here’s the abstract:

While the trend in machine learning has tended towards building more complicated (black box) models, such models are not as useful for high stakes decisions – black box models have led to mistakes in bail and parole decisions in criminal justice, flawed models in healthcare, and inexplicable loan decisions in finance. Simpler, interpretable models would be better. Thus, we consider questions that diametrically oppose the trend in the field: for which types of datasets would we expect to get simpler models at the same level of accuracy as black box models? If such simpler-yet-accurate models exist, how can we use optimization to find these simpler models? In this talk, I present an easy calculation to check for the poss