To Stop Algorithmic Bias, We First Have to Define It

This article identifies the absence of a clear definition of algorithmic bias as the primary culprit for the lack of government regulation of AI algorithms. When specific “goalposts” are established, the authors argue, regulators can provide sector and use case specific guidance, insist on appropriate risk management protocols, and hold companies responsible for instances of AI discrimination. The authors advocate for an output-based standard that focuses on identifying whether the algorithm’s prediction is accurate and equitable in order to simplify the oversight process.