A recently published Panel for the Future of Science and Technology (STOA) study examines the impact of biases on datasets used to support decision-making systems based on artificial intelligence. It explores the ethical implications of the deployment of digital technologies in the context of proposed European Union legislation, such as the AI act, the data act and the data governance act; as well as the recently approved Digital Services Act and Digital Markets Act. It ends by setting out a range of policy options to mitigate the pernicious effects of biases in decision-making systems that rely on machine learning. Machine learning (ML) is a form of artificial intelligence (AI) in which computers develop their own decision-making processes for situations that cannot be directly and satisfactorily addressed by available algorithms. The process is adjusted through the exploration of existing data on previous similar situations that include the solutions found at the time. The broader and more balanced the dataset is, the better the chances will be of obtaining a valid result; but there is no a priori way of knowing whether the data available will suffice to collect all aspects of the problem at hand.

Important note on nomenclature: While often referred to as "AI" registers, the more appropriate term should be "Algorithmic Decision System" (ADS) registers. A register is an official record of information. In its simplest form, it can provide visibility over algorithmic systems in use, which is the most basic form of transparency. It can also be designed to enable meaningful transparency that meets stakeholder information needs. It's the latter use case that I explore throughout this article. Meaningful transparency is an important concept in AI ethics.

All the sessions from Transform 2021 are available on-demand now. In early June, border officials "quietly deployed" the mobile app CBP One at the U.S.-Mexico border to "streamline the processing" of asylum seekers. While the app will reduce manual data entry and speed up the process, it also relies on controversial facial recognition technologies and stores sensitive information on asylum seekers prior to their entry to the U.S. The issue here is not the use of artificial intelligence per se, but what it means in relation to the Biden administration's pre-election promise of civil rights in technology, including AI bias and data privacy. When the Democrats took control of both House and Senate in January, onlookers were optimistic that there was an appetite for a federal privacy bill and legislation to stem bias in algorithmic decision-making systems. This is long overdue, said Ben Winters, Equal Justice Works Fellow of the Electronic Privacy Information Center (EPIC), who works on matters related to AI and the criminal justice system.

For those engaged in advocacy around the social harms of AI systems, a definitional exercise could, however, be a key way to rescue AI from the abstract, and foreground social and material concerns around these systems. Just as glossy data visualizations can obscure the unequal impacts and governance failures of the pandemic, AI as an abstract buzzword can be brandished against complex social problems as if it were a neutral and external'solution' rather than a sociotechnical system 14 designed and developed to make value-laden choices and trade-offs.

Artificial intelligence programs are extremely good at finding subtle patterns in enormous amounts of data, but don't understand the meaning of anything. Whether you are searching the Internet on Google, browsing your news feed on Facebook, or finding the quickest route on a traffic app like Waze, an algorithm is at the root of it. Algorithms have permeated our daily lives; they help to simplify, distill, process, and provide insights from massive amounts of data. According to Ernest Davis, a professor of computer science at New York University's Courant Institute of Mathematical Sciences whose research centers on the automation of common-sense reasoning, the technologies that currently exist for artificial intelligence (AI) programs are extremely good at finding subtle patterns in enormous amounts of data. "One way or another," he says, "that is how they work."