An AI-based computer system can gather data and use that data to make decisions or solve problems – using algorithms to perform tasks that, if done by a human, would be said to require intelligence. The benefits created by AI and machine learning (ML) systems for better health care, safer transportation, and greater efficiencies across the globe are already happening. But the increased amounts of data and computing power that enable sophisticated AI and ML models raise questions about the privacy impacts, ethical consequences, fairness, and real world harms if the systems are not designed and managed responsibly. FPF works with commercial, academic, and civil society supporters and partners to develop best practices for managing risk in AI and ML and assess whether historical data protection practices such as fairness, accountability, and transparency are sufficient to answer the ethical questions they raise.
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 […]
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 […]
Organizations must lead with privacy and ethics when researching and implementing neurotechnology: FPF and IBM Live event and report release
A New FPF and IBM Report and Live Event Explores Questions About Transparency, Consent, Security, and Accuracy of Data The Future of Privacy Forum (FPF) and the IBM Policy Lab released recommendations for promoting privacy and mitigating risks associated with neurotechnology, specifically with brain-computer interface (BCI). The new report provides developers and policymakers with actionable […]
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.
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 […]
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.
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.
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.
FPF Releases Understanding Facial Detection, Characterization, and Recognition Technologies and Privacy Principles for Facial Recognition Technology in Commercial Applications
These resources will help businesses and policymakers better understand and evaluate the growing use of face-based biometric technology systems when used for consumer applications. Facial recognition technology can help users organize and label photos, improve online services for visually impaired users, and help stores and stadiums better serve customers. At the same time, the technology often involves the collection and use of sensitive biometric data, requiring careful assessment of the data protection issues raised. Understanding the technology and building trust are necessary to maximize the benefits and minimize the risks.
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.