Future of Privacy Forum Awarded National Science Foundation and Department of Energy Grants to Advance White House Executive Order on Artificial Intelligence
The Future of Privacy Forum (FPF) has been awarded grants by the National Science Foundation (NSF) and the Department of Energy (DOE) to support FPF’s establishment of a Research Coordination Network (RCN) for Privacy-Preserving Data and Analytics. FPF’s work will support the development and deployment of Privacy Enhancing Technologies (PETs) for socially beneficial data sharing […]
Overcoming Hurdles to Effective Data Sharing for Researchers
In 2021, challenges faced by academics in accessing corporate data sets for research and the issues that companies were experiencing to make privacy-respecting research data available broke into the news. With its long history of research data sharing, FPF saw an opportunity to bring together leaders from the corporate, research, and policy communities for a conversation […]
Data Sharing … By Any Other Name
There are many different uses of the term “data sharing” to describe a relationship between parties who share data from one organization to another organization for a new purpose. Some uses of the term data sharing are related to academic and scientific research purposes, and some are related to transfer of data for commercial or government purposes. ..it is imperative that we are more precise which forms of sharing we are referencing so that the interests of the parties are adequately considered, and the various risks and benefits are appropriately contextualized and managed.
FPF Receives Grant To Design Ethical Review Process for Research Access to Corporate Data
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.
New White Paper Explores Privacy and Security Risk to Machine Learning Systems
FPF and Immuta Examine Approaches That Can Limit Informational or Behavioral Harms WASHINGTON, D.C. – September 20, 2019 – The Future of Privacy Forum (FPF) released a white paper, WARNING SIGNS: The Future of Privacy and Security in an Age of Machine Learning, exploring how machine learning systems can be exposed to new privacy and […]
Unfairness By Algorithm: Distilling the Harms of Automated Decision-Making
Analysis of personal data can be used to improve services, advance research, and combat discrimination. However, such analysis can also create valid concerns about differential treatment of individuals or harmful impacts on vulnerable communities. These concerns can be amplified when automated decision-making uses sensitive data (such as race, gender, or familial status), impacts protected classes, or affects individuals’ eligibility for housing, employment, or other core services. When seeking to identify harms, it is important to appreciate the context of interactions between individuals, companies, and governments—including the benefits provided by automated decision-making frameworks, and the fallibility of human decision-making.