Audibility, as it pertains to AI systems, is the system’s capacity to undergo an evaluation of the algorithms, data, and design processes specific to that system. A three-pronged approach is used to assess the performance and accuracy of AI: algorithms are examined to determine their effectiveness in generating outputs; data collected is scrutinized to ensure it is of sufficient quality to maintain accuracy; and the design processes utilized to create the system must meet certain standards. With these criteria in mind, diligent audibility can refurbish an AI system into its most efficient form.
