Why we need to audit algorithms and AI from end to end

The full picture of algorithmic risks and harms is a complicated one. So how do we approach the task of auditing algorithmic systems? There are various attempts to simplify the picture into overarching, standardized frameworks; or focus on particular areas, such as understanding and explaining the “black box” of models. While this work and thinking have benefits, we need to look at systems from end to end to fully capture the reality of algorithmic harms.