All Article Summaries

Apple’s privacy nutrition labels require app developers to complete a form to make data collection processes more transparent to users. Developers support more transparency, but found the labelling process challenging.

Sometimes users' online content or actions violate Internet service providers’ rules. Services should moderate content effectively, but should also promote free expression, competition, and community.

This article provides a road map for the study of algorithmic systems used by local governments in the United States, including issues relating to procurement, bias, transparency, and regulation.

Western scholars view China’s Social Credit System (SCS) as a repressive regime. However, liberal governments could adopt social credit systems to improve protection for rights and regulate data.

Federal and state agencies increasingly use automation and software to carry out their responsibilities, resulting in a loss of due process and accountability. However, some agencies use technology effectively.

Decision-making algorithms are increasingly used in administrative law, as well as in civil and criminal justice, raising due process concerns. Source code is protected from scrutiny by trade secret law.

When public-sector algorithms are used in key court functions like sentencing, lawyers should be prepared to challenge algorithmic processes as well as the accuracy of input data.

In many jurisdictions, police data is tainted by a history of racial bias, planted evidence, and distorted reporting. Predictive policing systems trained on this data may perpetuate bias.

Digital platforms alter the structure of markets and create complex business ecosystems. Platforms challenge traditional methods of management and regulation.

Algorithmic decision-making may perpetuate bias if the data used to train the system reflects bias. Thus far, regulators and the courts have not addressed algorithmic bias effectively.