Website/Cookie Privacy Policy
Our websites use cookies, some of which are necessary for the webpages you visit to function, while others provide us information for statistical purposes, are used to provide social media functionalities and a few are used by advertisers to personalize online ads. We also use web beacons, which is code that triggers browser to set […]
Privacy Features of iOS 12 and MacOS Mojave
With much media attention focused on new Apple hardware, including new iPhones, Apple also released updated versions of its mobile and desktop operating systems for public download this week. The software upgrades (iOS 12 for iPhones, and macOS 10.14 Mojave for desktop Macs) bring many new features, such as Group FaceTime, options to customize notifications, and aesthetic changes such as an optional desktop “Dark Mode.
If privacy principles are from Venus, then engineering rules are from Mars
FPF Advisory Board member, Alisa Bergman, Vice President, Chief Privacy Officer at
Adobe Systems, recently wrote an article in the IAPP Tech Privacy Advisor that we think is very useful. The article started from a presentation Bergman did for Adobe engineers.
AI and Machine Learning: Ethics, Governance, and Compliance Resources
The legal and regulatory landscape for AI and ML systems is changing rapidly. The list of resources here reflects the leading thinking from academics, regulatory agencies, and on-going projects and studies to provide the best guidance to commercial and public entities on implementing AI into their products and services. I. General AI & Ethics Resources […]
AI and Machine Learning: Educational Resources
Content Areas Interactive Explanations and Courses News, Reports, and Other Media International Resources Books Interactive Explanations and Courses Google Machine Learning 101 – A comprehensive overview of AI and machine learning with numerous resources for additional research. Intro to Machine Learning – A detailed, video-based, interactive course into ML concepts. Prerequisites include strong algebra skills […]
Privacy Best Practices for Consumer Genetic Testing Services
The Best Practices provide a policy framework for the collection, protection, sharing, and use of Genetic Data generated by consumer genetic testing services. These services are commonly offered to consumers for testing and interpretation related to ancestry, health, nutrition, wellness, genetic relatedness, lifestyle, and other purposes.
Future of Privacy Forum and Leading Genetic Testing Companies Announce Best Practices to Protect Privacy of Consumer Genetic Data
Washington, DC – Today, Future of Privacy Forum, along with leading consumer genetic and personal genomic testing companies 23andMe, Ancestry, Helix, MyHeritage, and Habit, released Privacy Best Practices for Consumer Genetic Testing Services. The Best Practices provide a policy framework for the collection, protection, sharing, and use of Genetic Data generated by consumer genetic testing services. These services are commonly offered to consumers for testing and interpretation related to ancestry, health, nutrition, wellness, genetic relatedness, lifestyle, and other purposes.
PrivacyNews.TV
FPF uses Facebook Live to discuss timely topics. Watch some of our previous videos here and be sure to tune in live for our next discussion!
Mobile Platforms Address Data Privacy with 2018 Updates (iOS 12, Mojave, & Android P)
In light of recent debates over Facebook’s role in protecting users’ privacy against third-party app developers, many are recognizing the importance of mobile platforms in safeguarding user data. Apple emphasized privacy in its Worldwide Developers Conference (June 4-8, 2018), highlighting several privacy-related updates to the upcoming macOS and iOS 12. Google also made privacy a focus of their newest mobile operating system, Android P, with several key software updates that will restrict app developers’ access to data.
Immuta and the Future of Privacy Forum Release First-Ever Risk Management Framework for AI and Machine Learning
College Park, MD – June 26, 2018 – Immuta and the Future of Privacy Forum (FPF) today announced the first-ever framework for practitioners to manage risk in artificial intelligence (AI) and machine learning (ML) models. Their joint whitepaper, Beyond Explainability: A Practical Guide to Managing Risk in Machine Learning Models, provides business executives, data scientists, and compliance professionals with a strategic guide for governing the legal, privacy, and ethical risks associated with this technology.