Google researchers release audit framework to close AI accountability gap

Researchers associated with Google and the Partnership on AI have created a framework to help companies and their engineering teams audit AI systems before deploying them. The framework, intended to add a layer of quality assurance to businesses launching AI, translates into practice values often espoused in AI ethics principles and tackles an accountability gap authors say exists in AI today. The work, titled "Closing the AI Accountability Gap: Defining an End-to-End Framework for Internal Algorithmic Auditing" is one of a handful of outstanding AI ethics research papers accepted for publication as part of the Fairness, Accountability, and Transparency (FAT) conference, which takes place this week in Barcelona, Spain. "The proposed auditing framework is intended to contribute to closing the development and deployment accountability gap of large-scale artificial intelligence systems by embedding a robust process to ensure audit integrity," the paper reads. "At a minimum, the internal audit process should enable critical reflections on the potential impact of a system, serving as internal education and training on ethical awareness in addition to leaving what we refer to as a'transparency trail' of documentation at each step of the development cycle." The framework is also intended to identify risks and reduce them to the lowest degree possible, as well as to map out how things that can be done differently in the future or how to respond to a failure after launch.