Growing recognition of the potential for exploitation of personal data and of the shortcomings of prior privacy regimes has led to the passage of a multitude of new privacy regulations. Some of these laws—notably the European Union’s General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA)—have been the focus of large bodies of research by the computer science community, while others have received less attention. In this work, we analyze a set of 24 privacy laws and data protection regulations drawn from around the world—both those that have frequently been studied by computer scientists and those that have not—and develop a taxonomy of rights granted and obligations imposed by these laws. We then leverage this taxonomy to systematize 270 technical research papers published in computer science venues that investigate the impact of these laws and explore how technical solutions can complement legal protections. Finally, we analyze the results in this space through an inter-disciplinary lens and make recommendations for future work at the intersection of computer science and legal privacy.

Specialized documentation techniques have been developed to communicate key facts about machine-learning (ML) systems and the datasets and models they rely on. Techniques such as Datasheets, AI FactSheets, and Model Cards have taken a mainly descriptive approach, providing various details about the system components. While the above information is essential for product developers and external experts to assess whether the ML system meets their requirements, other stakeholders might find it less actionable. In particular, ML engineers need guidance on how to mitigate potential shortcomings in order to fix bugs or improve the system's performance. We propose a documentation artifact that aims to provide such guidance in a prescriptive way. Our proposal, called Method Cards, aims to increase the transparency and reproducibility of ML systems by allowing stakeholders to reproduce the models, understand the rationale behind their designs, and introduce adaptations in an informed way. We showcase our proposal with an example in small object detection, and demonstrate how Method Cards can communicate key considerations that help increase the transparency and reproducibility of the detection model. We further highlight avenues for improving the user experience of ML engineers based on Method Cards.

Hello Barbie, CogniToys Dino, and Amazon Echo are part of a new wave of connected toys and gadgets for the home that listen. Unlike the smartphone, these devices are always on, blending into the background until needed. We conducted interviews with parent-child pairs in which they interacted with Hello Barbie and CogniToys Dino, shedding light on children's expectations of the toys' "intelligence'" and parents' privacy concerns and expectations for parental controls. We find that children were often unaware that others might be able to hear what was said to the toy, and that some parents draw connections between the toys and similar tools not intended as toys (e.g., Siri, Alexa) with which their children already interact. Our findings illuminate people's mental models and experiences with these emerging technologies and will help inform the future designs of interactive, connected toys and gadgets. We conclude with recommendations for parents, designers, and policy makers.