ProPublica employed an algorithmic audit to unveil inherent racial biases within Northpointe’s risk assessment software. Independently testing the system with diverse respondents statistically exposed its bias against black individuals. Utilizing a straightforward visual bar graph, they vividly illustrated how the system favored white defendants with lower-risk ratings compared to black defendants. The article’s intended audience comprises Northpointe’s software developers, policymakers, programmers, black activists, social justice advocates, law enforcement, researchers, the judiciary, academia, the media, and a broader public. However, the limitation of this approach lies in raising awareness alone; rectifying these biases demands purposeful stakeholder engagement and consistent efforts.

In the ACM conference paper titled “Accountability in an Algorithmic Society: Relationality, Responsibility, and Robustness in Machine Learning,” the authors offer a comprehensive framework to understand the intricacies of enforcing accountability in the digital age. They draw from Nissenbaum’s moral philosophy, political theory, and social sciences, examining four accountability barriers as proposed by Nissenbaum: “Many hands,” “bug,” “computer as scapegoat,” and “ownership without liability.” They effectively elucidate how these barriers obscure accountability, primarily concerning “who is accountable,” “For what,” “To whom,” and “under what circumstances.” The authors also provide solutions to “weakening the barriers,” including developing rigorous care standards and defining acceptable levels of adverse outcomes. The paper assumes that accountability is a universal good and therefore does not spend any effort at convincing actors on the need for it. It also fails to consider accountability from the economic standpoint as this would have a greater appeal to the creators of these computer systems who are mainly motivated by economic gains.

The AI comics excel in simplifying complex AI concepts, employing everyday analogies to enhance reader comprehension. They serve as valuable educational tools, bridging the gap between non-technical audiences and experts looking to convey intricate technical phenomena to the general public. The use of images fosters immersion and better concept comprehension. Nonetheless, one drawback, is the oversimplification of intricate concepts. While beneficial for educating beginners, they may fall short in conveying the nuances and complexities of AI and machine learning to a more in-depth understanding.