Responsible gambling (RG) algorithms appear to have become more complex and nuanced over time, with “AI” and “machine learning” serving as buzz words to signal their legitimacy. However, no matter how complex our models, they are only as valid as the data and outcomes upon which they are built and validated. This talk entreats the audience to return to basics and consider both the building blocks and pitfalls of RG algorithms. The talk will touch on the outcomes used to validate and evaluate these algorithms, how we distinguish involvement from risk for problems, potential algorithmic biases, and the proper use of algorithms within a larger RG system.