Artificial Intelligence

The FPF Privacy Book Club provides members with the opportunity to read a wide range of books — privacy, data, ethics, academic works, and other important data relevant issues — and have an open discussion of the selected literature. For January, we will discuss We, the Robots? Regulating Artificial Intelligence and the Limits of the […]

Lawyers are trained to respond to risks that threaten the market position or operating capital of their clients. However, when it comes to AI, it can be difficult for lawyers to provide the best guidance without some basic technical knowledge. This article shares some key insights from our shared experiences to help lawyers feel more at ease responding to AI questions when they arise. 

BCIs are computer-based systems that directly record, process, analyze, or modulate human brain activity in the form of neurodata that is then translated into an output command from human to machine. Neurodata is data generated by the nervous system, composed of the electrical activities between neurons or proxies of this activity. When neurodata is linked, or reasonably linkable, to an individual, it is personal neurodata. 

This paper outlines the spectrum of AI technology, from rules-based and symbolic AI to advanced, developing forms of neural networks, and seeks to put them in the context of other sciences and disciplines, as well as emphasize the importance of security, user interface, and other design factors.

In legislatures across the United States, state lawmakers are introducing proposals to govern the uses of automated decision-making systems (ADS) in record numbers. In contrast to comprehensive privacy bills that would regulate collection and use of personal information, automated decision-making system (ADS) bills in 2021 specifically seek to address increasing concerns about racial bias or […]

On Wed., April 14th, FPF hosted an expert panel discussion on “AI Out Loud: Representation in Data for Voice-Activated Devices, Assistants.” FPF’s Senior Counsel and Director of AI and Ethics, Brenda Leong, moderated the panel featuring Anne Toth, the Director of Alexa Trust, Amazon; Irina Raicu, Internet Ethics Program Director, Markkula Center for Applied Ethics, […]

By Pollyanna Sanderson (Policy Counsel), Katelyn Ringrose (Christopher Wolf Diversity Law Fellow) & Stacey Gray (Senior Policy Counsel)   Today, on the first day of a rapid-fire 2020 legislative session in the state of Washington, State Senator Carlyle has introduced a new version of the Washington Privacy Act (WPA). Legislators revealed the Act during a live press […]

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 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. 

On Friday, June 14, FPF submitted a letter to the New York State Assembly and Senate supporting a well-crafted moratorium on facial recognition systems for security uses in public schools.