Future of Privacy Forum (FPF) has received a grant to create an independent party of experts for an ethical review process that can provide trusted vetting of corporate-academic research projects. FPF will establish a pool of respected reviewers to operate as a standalone, on-demand review board to evaluate research uses of personal data and create a set of transparent policies and processes to be applied to such reviews.
Posts by Brenda Leong
FPF is working with Immuta and others to explain the steps machine learning creators can take to limit the risk that data could be compromised or a system manipulated.
The leap from 3G to 4G technology brought with it faster data transfer speeds, which supported widespread adoption of data cloud and streaming services, video conferencing, and Internet of Things devices such as digital home assistants and smartwatches. 5G technology has the potential to enable another wave of smart devices: always connected and always communicating to provide faster, more personalized services.
The media has recently labeled manipulated videos of people “deepfakes,” a portmanteau of “deep learning” and “fake,” on the assumption that AI-based software is behind them all. But the technology behind video manipulation is not all based on deep learning (or any form of AI), and what are lumped together as deepfakes actually differ depending on the particular technology used. So while the example videos above were all doctored in some way, they were not all altered using the same technological tools, and the risks they pose – particularly as to being identifiable as fake – may vary.
The reasons for the development and inclusion of these clauses, and the privacy controversies the terms can trigger, tell an interesting tale about the intersection of data protection and intellectual property law.
The opening session of FPF’s Digital Data Flows Masterclass provided an educational overview of Artificial Intelligence and Machine Learning – featuring Dr. Swati Gupta, Assistant Professor in the H. Milton Stewart School of Industrial and Systems Engineering at Georgia Tech; and Dr. Oliver Grau, Chair of ACM’s Europe Technology Policy Committee, Intel Automated Driving Group, […]
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.
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.
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.
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.