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A Thoughtful Discussion of Privacy Issues Raised by AI and Machine Learning
Recently, the Future of Privacy Forum and the Brookings Institution held a discussion session bringing together Hill staff, industry representatives and civil society groups. The conversation was guided by Cam Kerry of Brookings and Brenda Leong and John Verdi from FPF. Topics included whether AI and machine learning issues should be covered in a comprehensive […]
Fairness, Ethics, & Privacy in Tech: A Discussion with Chanda Marlowe
After beginning her career as a high school English teacher, Chanda Marlowe’s career change led her to become FPF’s inaugural Christopher Wolf Diversity Law Fellow. She’s an expert on location and advertising technology, algorithmic fairness, and how vulnerable populations can be uniquely affected by privacy issues. What led you to the Future of Privacy Forum? I […]
Artificial Intelligence: Privacy Promise or Peril?
Understanding AI and its underlying algorithmic processes presents new challenges for privacy officers and others responsible for data governance in companies ranging from retailers to cloud service providers. In the absence of targeted legal or regulatory obligations, AI poses new ethical and practical challenges for companies that strive to maximize consumer benefits while preventing potential harms.
AI and Machine Learning: Perspectives with FPF’s Brenda Leong
As we prepare to toast our 10th anniversary, we’re hearing from FPF policy experts about important privacy issues. Today, Brenda Leong, FPF Senior Counsel and Director of Strategy, is sharing her perspective on AI and machine learning. Brenda also manages the FPF portfolio on biometrics, particularly facial recognition, and oversees strategic planning for the organization.Tell […]
FPF Partner in algoaware Project Releases State of the Art Report
algoaware has released the first public version of the State of the Art Report, open for peer review. The report includes a comprehensive explanation of the key concepts of algorithmic decision-making, a summary of the academic debate and its most pressing issues, as well as an overview of the most recent and relevant initiatives and policy actions of the civil society as well as of national and international governing bodies.
Calls for Regulation on Facial Recognition Technology
We look forward to working with Microsoft, others in industry, and policymakers to “create policies, processes, and tools” to make responsible use of Facial Recognition technology a reality.
Nothing to Hide: Tools for Talking (and Listening) About Data Privacy for Integrated Data Systems
Data-driven and evidence-based social policy innovation can help governments serve communities better, smarter, and faster. Integrated Data Systems (IDS) use data that government agencies routinely collect in the normal course of delivering public services to shape local policy and practice. They can use data to evaluate the effectiveness of new initiatives or bridge gaps between public services and community providers.
Nothing to Hide: Tools for Talking (and Listening) About Data Privacy for Integrated Data Systems Report
Data-driven and evidence-based social policy innovation can help governments serve communities better, smarter, and faster. Integrated Data Systems (IDS) use data that government agencies routinely collect in the normal course of delivering public services to shape local policy and practice. They can use data to evaluate the effectiveness of new initiatives or bridge gaps between public services and community providers.
The Privacy Expert's Guide to AI And Machine Learning
Today, FPF announces the release of The Privacy Expert’s Guide to AI and Machine Learning. This guide explains the technological basics of AI and ML systems at a level of understanding useful for non-programmers, and addresses certain privacy challenges associated with the implementation of new and existing ML-based products and services.
The Privacy Expert’s Guide to AI and Machine Learning Report
Today, FPF announces the release of The Privacy Expert’s Guide to AI and Machine Learning. This guide explains the technological basics of AI and ML systems at a level of understanding useful for non-programmers, and addresses certain privacy challenges associated with the implementation of new and existing ML-based products and services.