Understanding black box AI and its alternatives

Black box AI refers to systems where users can see inputs and outputs but not how decisions are made internally. These hidden systems appear frequently in healthcare, finance, and criminal justice. However, research consistently shows that simpler, transparent models often perform just as well as complex ones. For example, in criminal recidivism prediction, simple interpretable models using age and criminal history match the accuracy of proprietary black box systems. When designing AI interfaces, question whether a black box is truly necessary or if it's being used due to assumptions about performance.

This perspective shift can lead to more trustworthy AI systems without sacrificing accuracy, particularly for decisions with significant human impact. Rather than accepting black boxes as inevitable for complex problems, we should assume interpretable alternatives exist until definitively proven otherwise.^[1]