It’s doubtful that anyone would set out to build ML models that don’t meet their users’ needs, are biased, or inaccurate. Instead, teams likely find themselves here due to a general lack of knowledge. Individuals and companies focus on the technical tools and know-how to build ML and miss the equally crucial human side. What can be done to help prevent ML catastrophes? This article discusses the critical points and considerations in the ML workflow where UX research should be included.