NEW FPF REPORT: Confidential Computing and Privacy: Policy Implications of Trusted Execution Environments
Written by Judy Wang, FPF Communications Intern Today, the Future of Privacy Forum (FPF) published a paper on confidential computing, a privacy-enhancing technology (PET) that marks a significant shift in the trustworthiness and verifiability of data processing for the use cases it supports, including training and use of AI models. Confidential computing leverages two key […]
Future of Privacy Forum and Leading Companies Release Best Practices for AI in Employment Relationships
Expert Working Group Focused on AI in Employment Launches Best Practices that Promote Non-Discrimination, Human Oversight, Transparency, and Additional Protections. Today, the Future of Privacy Forum (FPF), with ADP, Indeed, LinkedIn, and Workday — leading hiring and employment software developers — released Best Practices for AI and Workplace Assessment Technologies. The Best Practices guide makes […]
Newly Updated Report: The Spectrum of Artificial Intelligence – Companion to the FPF AI Infographic
Today, we are re-releasing the report: The Spectrum of Artificial Intelligence – Companion to the FPF AI Infographic with new updates to account for the development and use of advanced generative AI tools. In December 2020, FPF published the Spectrum of Artificial Intelligence – An Infographic Tool, designed to visually display the variety and complexity […]
FPF Report: Automated Decision-Making Under the GDPR – A Comprehensive Case-Law Analysis
On May 17, the Future of Privacy Forum launched a comprehensive Report analyzing case-law under the General Data Protection Regulation (GDPR) applied to real-life cases involving Automated Decision Making (ADM). The Report is informed by extensive research covering more than 70 Court judgments, decisions from Data Protection Authorities (DPAs), specific Guidance and other policy documents […]
The Spectrum of AI: Companion to the FPF AI Infographic
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.
New White Paper Provides Guidance on Embedding Data Protection Principles in Machine Learning
Immuta and the Future of Privacy Forum (FPF) today released a working white paper, Data Protection by Process: How to Operationalise Data Protection by Design for Machine Learning, that provides guidance on embedding data protection principles within the life cycle of a machine learning model. Data Protection by Design (DPbD) is a core data protection requirement […]
Warning Signs: Identifying Privacy and Security Risks to Machine Learning Systems
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 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.
Policy Brief: European Commission’s Strategy for AI, explained
The European Commission published a Communication on “Artificial Intelligence for Europe” on April 24th 2018. It highlights the transformative nature of AI technology for the world and it calls for the EU to lead the way in the approach of developing AI on a fundamental rights framework. AI for good and for all is the motto the Commission proposes. The Communication could be summed up as announcing concrete funding for research projects, clear social goals and more thinking about everything else.
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.