Bias in law enforcement has long been a problem in America. The killing of George Floyd, an unarmed Black man, by Minneapolis police officers in May 2020 most recently brought attention to this fact--sparking waves of protest across the country, and highlighting the ways in which those who are meant to "serve and protect" us do not serve all members of society equally. With the dawn of artificial intelligence (AI), a slew of new machine learning tools promise to help protect us--quickly and precisely tracking those who may commit a crime before it happens--through data. Past information about crime can be used as material for machine learning algorithms to make predictions about future crimes, and police departments are allocating resources towards prevention based on these predictions. The tools themselves, however, present a problem: The data being used to "teach" the software systems is embedded with bias, and only serves to reinforce inequality.

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.

They are becoming more sophisticated, useful, and pervasive. Owing in part to the rapid advancement of powerful algorithms, AI has created not only new business opportunities worldwide, but also concerns from consumers, policymakers anddevelopers of the technology. These concerns need to be addressed. In fact, practitioners of data science, big data, and machine learning have been actively addressing social and ethical concerns that pertain to our increasingly algorithmic society. Can learning algorithms be designed to be fair?