Sophisticated models that learn patterns from large datasets offer the promise of providing impartial, efficient, accurate, "smart" decision-makers. As such, they are becoming more widespread and have a lot of influence over people's life outcomes. Weapons of Math Destruction argues, however, that these algorithms have troubling features that codify unjust discrimination and are inscrutable and unaccountable. I focus in this post on these undesirable features and how to counteract them.
The benefits of intersectionality I put forward are more apparent when given math analogies. Set intersection provides an easy reminder that choosing both is a wise idea. Conceptualizing oppression as a multidimensional space discourages Oppression Olympics. Intersectional analyses are good/responsible practice in the same way that checking for and reporting significant interactions is in statistical analysis.