This paper outlines the spectrum of AI technology, from rules-based and symbolic AI to advanced, developing forms of neural networks.
In this paper we discuss why data minimization is key to protecting privacy and explain how it can reduce the harm of data collection and exploitation.
Privacy design strategies aim to translate vague legal norms in concrete design requirements. They provide talking points to explore the design of the system. They guide the initial design
sketches into a privacy-friendly direction, forcing one to make fundamental design choices early on.
This report aims to raise the awareness of the general audience and serve as a resource to policy shapers and practitioners in the public and private sectors to help frame the debate around data.
This document establishes guidelines for the encryption and use of user-provided IDs – notably email addresses and phone numbers – in scenarios when online publishers or marketers offer personalized content or services tied to a user-provided email or phone number.
This short whitepaper aims to create the beginnings of a framework for best practices standards by focusing on specific privacy and security vulnerabilities within ML systems. At present, we view these vulnerabilities as warning signs—either of a future in which the benefits of ML are not fully embraced, or a future in which ML’s liabilities […]