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As policymakers and legal privacy folks wade through the challenges of machine unlearning for data deletion, AI systems continue to evolve, creating wider gaps between privacy and AI governance law and regulations and the tech used to respond to it. Jevan Hutson is one of those rare legal professionals with a firm grasp on the shifting sands of AI and machine unlearning from a technical perspective. He understands how unlearning works and how it doesn't, and he connects the dots between the theoretical side of law and the practical sides of AI governance methods like unlearning. In this talk, Hutson explains the different forms or levels of machine unlearning, its caveats and limitations. Plus, he digs into his recent work on what he and his colleagues call AI entanglement, the knotted layers of AI-based elements that live inside AI models, things that make governing these systems more and more complex.

Machine unlearning is a broad category encompassing a variety of techniques intended to remove - or lessen - the presence of sensitive information or concepts such as personal data, intellectual property like branded content and even artistic styles from machine learning models. In the privacy context, machine unlearning is often discussed in relation to the European Union's General Data Protection Regulation (GDPR) and its AI Act, as well as the California Consumer Privacy Act. But the chasm between what some in the policy world might believe machine unlearning can do and the actual technical capabilities of these methods is wide.  This second episode in World Privacy Forum's series on machine unlearning features Kairan Zhao, a PhD candidate at the University of Warwick in the UK focused on unlearning, privacy and AI safety. In this talk she and WPF Deputy Director Kate Kaye discuss key elements of machine unlearning and areas where these techniques are applied. Zhao explains why there could be a long way to go before machine unlearning can be used at scale as an effective AI privacy tool. The talk in this episode was recorded in March 2026 in Tucson, Arizona at the Winter Conference on Applications of Computer Vision (WACV) where World Privacy Forum held its third WACV privacy tutorial. Kairan Zhao's related machine unlearning research: What Makes Unlearning Hard and What to Do About It, Zhao et al., NeurIPS 2024 Scalability of Memorization-Based Machine Unlearning, Zhao et al., FITML @ NeurIPS 2024 Are We Making Progress in Unlearning? Findings from the First NeurIPS Unlearning Competition, Triantafillou et al.  Featured in this episode: Kairan Zhao, PhD Candidate, Machine Learning, University of Warwick Dr. Yezhou "YZ" Yang, tenured Associate Professor in Computer Science and Engineering in the School of Computing and Augmented Intelligence at Arizona State University Jevan Hutson, Director of the Technology Law and Public Policy Clinic, and Acting Assistant Professor at the University of Washington School of Law Kate Kaye, Deputy Director of World Privacy Forum The Privacy on the Ground intro theme features music by Pangal. Episode music is by Maciej Sadowski.

Machine Unlearning is a field of AI research gaining attention in the policy world. It's often discussed in the context of privacy, particularly in relation to the European Union's General Data Protection Regulation (GDPR) and the EU AI Act as well as the California Consumer Privacy Act (CCPA). At World Privacy Forum's tutorial at the Winter Conference on Applications of Computer Vision (WACV) in Tucson, Arizona in March 2026, we aimed to bridge gaps in understanding between the technical academic community, including machine unlearning researchers, and the AI governance, policy, and legal communities. In this episode you'll hear a chat with Dr. Yezhou "YZ" Yang, a tenured associate professor in computer science and engineering in the School of Computing and Augmented Intelligence at Arizona State University; Yang spoke at WPF's tutorial about machine unlearning for concept erasure. Topics discussed:  Why machine unlearning for concept erasure isn't just for privacy Benchmarks for testing the removal of data - or even the presence of a visual artist's style - from AI models Why Yang wants to see more inclusion of technical AI practitioners in the policy conversation Research discussed:  EraseFlow: Learning Concept Erasure Policies via GFlowNet-Driven Alignment R.A.C.E. : Robust Adversarial Concept Erasure for Secure Text-to-Image Diffusion Model WOUAF: Weight Modulation for User Attribution and Fingerprinting in Text-to-Image Diffusion Models Featured in this episode:
 Dr. Yezhou "YZ" Yang, tenured Associate Professor in Computer Science and Engineering in the School of Computing and Augmented Intelligence at Arizona State University Kate Kaye, Deputy Director of World Privacy Forum The Privacy on the Ground intro theme features music by Pangal. Episode music is by Maciej Sadowski.

The shift to generative AI has exposed the gaps between data and AI governance policy and real world uses and impacts of generative AI. Jason Tamara Widjaja can see the disconnects first hand from his perch leading the AI research and AI governance team as Executive Director of AI at MSD - the global biopharma company some in the US and Canada know better by the name Merck & Co. In this talk recorded in August 2025 Tamara Widjaja discusses the reasons why generative AI created new needs for assistance from AI Verify on AI governance tooling and services, about the challenges of scaling AI testing and selecting appropriate metrics, the importance of inspecting a full system in which AI models interact rather than just standalone models, and the potential lack of access to models and systems used in the highest risk scenarios.   The Privacy on the Ground intro theme features music by Pangal. Episode music is by Surly Gates. Featured in this episode: -Jason Tamara Widjaja, Executive Director of AI at MSD (a.k.a. Merck and Co.) -Kate Kaye, Deputy Director of World Privacy Forum

April Chin is co-CEO of AI assessment company Resaro, one of a small group of invite-only "premier" members of Singapore's AI governance program, AI Verify. In this second episode in Privacy on the Ground's series about AI Verify, Chin discusses the evolution of AI Verify, the methods and approaches it has developed and piloted, recent initiatives such as its generative AI-focused global AI Assurance Pilot, and the obstacles that external auditors like Resaro might face when it comes to accessing important components of AI systems needed for meaningful assessment. The interview was recorded in November 2025. The Privacy on the Ground intro theme features music by Pangal. Episode music is by Zefyr. Featured in this episode: -April Chin, Co-CEO of Resaro -Kate Kaye, Deputy Director of World Privacy Forum

Some might consider AI Verify's "building the plane as it flies" approach to AI governance to be rushed or risky; some might question the organization's deep collaboration with industry. But no matter what, it's hard to argue that Singapore's AI Verify project -- and its unique policy sandbox style model -- is influential and offers a real-world example of AI governance that is willing to test and iterate much in the same way that the tech industry itself does. In this first episode of our three-part series on Singapore's AI Verify, listeners will learn about the history and evolution of AI Verify and its key projects, and get a glimpse from insiders into one of its most recent efforts aimed at assessing generative AI applications.  The Privacy on the Ground intro theme features music by Pangal. Episode music is by Gianpaolo Rinaldi. Featured in this episode: April Chin, Co-CEO of Resaro Jason Tamara Widjaja, Executive Director of AI at MSD Kate Kaye, Deputy Director of World Privacy Forum

In part three of World Privacy Forum's explainer series about C2PA, we'll talk trust. C2PA is not intended to directly determine the trustworthiness of content, but it does have a Trust Model and C2PA metadata are meant to be used as signals for measuring content trustworthiness. In this episode, you'll learn about the C2PA Trust Model process including the Conformance Program, Trust List, and why the absence of C2PA metadata can affect how trustworthiness of content is measured. This episode of Privacy on the Ground features music by Liam Back and Speedtest. The Privacy on the Ground intro theme features music by Pangal. Featured in this episode: Kate Kaye, Deputy Director of World Privacy Forum

If you're looking for an accessible overview of how C2PA - aka Content Credentials - works technically and how it relates to privacy, identity, and trust, this is it! In our first episode in this explainer series about C2PA, we shared an overview of how C2PA works, and what it does in relation to privacy, identity, and trust. In this second episode, we dig deeper into Identity in C2PA. You'll learn about what sorts of users want identity to be attached to content metadata – and who doesn't, and about the risks and tradeoffs of identity in relation to C2PA. And you'll hear from Pam Dixon, World Privacy Forum's executive director, about existing digital identity systems, regulations and infrastructure around the world and how they could relate to C2PA. This episode of Privacy on the Ground features music by Liam Back and Speedtest. The Privacy on the Ground intro theme features music by Pangal.  Featured in this episode: Kate Kaye, Deputy Director of World Privacy Forum Pam Dixon, Executive Director of World Privacy Forum

If you're looking for an accessible overview of how C2PA works technically and how it relates to privacy, identity, and trust, this is it! Imagine a system that automatically generates detailed data showing where the digital images, videos and documents we encounter came from, who made them, how they have changed, who owns the rights to their use, and even whether AI was used in their creation. Some say C2PA (Coalition for Content Provenance and Authenticity) promises to be just that.  C2PA is a technical framework for connecting digital media content such as images and videos to data about the origins of and changes made to that content. But it is not just a "content labeling" system. C2PA is intended to provide signals for gauging trustworthiness of content, kind of like provenance documentation indicating the authenticity of an oil painting or showing how some ancient artifact changed hands over time. But how does C2PA really work? How does it relate to privacy, identity, and trust? And what could its use mean for our information and data ecosystem? It's too early to know whether C2PA will be one of those behind-the-scenes systems that shift the tectonic plates of our digital media ecosystem. But it's the right time to take a step back and assess what we do know about C2PA and what it could mean – not just for the future of digital information but for our connections to it. This episode of Privacy on the Ground features music by Liam Back and Speedtest. The Privacy on the Ground intro theme features music by Pangal.

We know from World Privacy Forum's 2023 report on AI governance tools, Risky Analysis, that these tools can have problems and should be assessed before they're deployed. But what do we learn about AI governance tools when they are actually put to use? This was the focus of recent research discussed in a paper by World Privacy Forum deputy director Kate Kaye. In this short episode of Privacy on the Ground, Kaye discusses her research which she presented recently at the fourth European Workshop on AI Fairness, an academic conference in The Netherlands. This episode of Privacy on the Ground features music by Maciej Sadowski. The Privacy on the Ground intro theme features music by Pangal. Read Kate Kaye's paper, "Uncovering Areas for AI Governance Tools Refinement through Real-World Use Case Analysis from Canada, Chile and Singapore," in the Proceedings of Fourth European Workshop on Algorithmic Fairness: https://proceedings.mlr.press/v294/kaye25a.html Read World Privacy Forum's 2023 report on AI governance tools, "Risky Analysis: Assessing and Improving AI Governance Tools, An international review of AI Governance Tools and suggestions for pathways forward": https://worldprivacyforum.org/posts/new-report-risky-analysis-assessing-and-improving-ai-governance-tools/

Will Tao knows first-hand how automated, algorithmic and machine learning systems used by Canada's government affect lives. The Founder of Heron Law Offices in Burnaby, British Columbia and co-founder of AIMICI (the AI Monitor for Immigration in Canada and Internationally) practices immigration, refugee and citizenship law in Canada. He has watched as these systems automatically determine or inform decisions affecting the lives of his clients sometimes influencing decisions about whether they can legally work, and even whether they must separate from their spouses or children. In this interview recorded in November 2024, World Privacy Forum's Kate Kaye talks with Tao about how use of algorithmic systems by Canada's immigration agency affect his clients, his experiences with Canada's Algorithmic Impact Assessments, and what he hopes to see change in relation to AI use and AI governance in Canada. Featured in this episode: Will Tao, Founder of Heron Law Offices in Burnaby, British Columbia and co-founder of AIMICI (the AI Monitor for Immigration in Canada and Internationally) Kate Kaye, Deputy Director of World Privacy Forum This episode of Privacy on the Ground features music by Maciej Sadowski. The Privacy on the Ground intro theme features music by Pangal.

When governments create AI governance policy tools, how are they used in real-world situations? What does the process of assessing a machine learning model used by a government agency look like? In this episode of Privacy on the Ground, you'll hear all about it from an insider: Mariana Germán, a researcher in the Ethical Algorithms Project at GobLab UAI, the public innovation laboratory at Chile's Universidad Adolfo Ibáñez's School of Government. Germán and the team at GobLab helped assess a machine learning model in development for use to help decide medical claims at Chile's health agency, the Department of Social Security Superintendence or SUSESO. In this full interview recorded in September of 2024, Germán and World Privacy Forum Deputy Director Kate Kaye dig deep into the metrics and measurements used to assess the model and its risks of producing discriminatory decisions, discussing the caveats of the AI governance tools, measures and metrics themselves, and how they were applied. Featured in this episode: Mariana Germán, researcher in the Ethical Algorithms Project at GobLab, the public innovation laboratory at Chile's Universidad Adolfo Ibáñez's School of Government WPF's deputy director and Privacy on the Ground host and producer Kate Kaye This episode of Privacy on the Ground features music by Maciej Sadowski. The Privacy on the Ground intro theme features music by Pangal.

Inside Chile's Department of Social Security Superintendence — the country's social security and medical insurance agency — medical claims processors hold the livelihoods and future health of thousands of people in their hands. They are responsible for deciding whether or not the government should pay wages when workers are on medical leave or cover other expenses such as occupational mental health related costs.  Like many government agencies these days, the agency, known by its acronym SUSESO, has begun to use machine learning models to help its limited staff process a high volume of medical claims. The idea is to streamline and in some cases automate certain parts of that claims evaluation process. Use of AI and the tools used to govern and assess these systems have upended traditional government processes. And in Chile, SUSESO project manager Rodrigo Moya is caught in the middle. Moya heads up the Digital Transformation, Innovation and Project Unit in SUSESO's Technology and Operations Department. He must balance project time and resource constraints with the need to analyze risks and impacts of AI.  In this episode of Privacy on the Ground, you'll hear the story of how Moya and others at SUSESO have used Chile's AI governance tool requiring assessment of AI systems as part of the AI procurement process, and about how Moya has navigated tensions regarding use of automation when it comes to risky government decision making affecting people's lives.  Featured in this episode: Rodrigo Moya, head of the Digital Transformation, Innovation and Project Unit in the Technology and Operations Department at SUSESO Mariana German, researcher in the Ethical Algorithms Project at GobLab, the public innovation laboratory at Chile's Universidad Adolfo Ibáñez's School of Government WPF's deputy director and Privacy on the Ground host and producer Kate Kaye This episode of Privacy on the Ground features music by Maciej Sadowski. The Privacy on the Ground intro theme features music by Pangal.

There's no shortage of principles and policies for governing AI from governments and NGOs around the world. But how do they put those principles and policies into practice? It's that practical side of AI governance that has been a key focus of our work at World Privacy Forum for more than two years.  Rather than look only at government policies, in early 2023 we went layers deeper, looking at the tools that governments and NGOs around the world—from Canada to Chile to Ghana to New Zealand to Singapore—have developed for actually implementing those AI policies.  Since then, we have observed actual use of these tools to understand how they govern and measure AI and spot where there's room for improvement. Key to that work has been talking to people who have actually used those AI governance tools, including people in Canada and Chile.  WPF's forthcoming Privacy on the Ground series—AI Governance Tools on the Ground—features talks with some of those people. In this episode introducing the series, you'll hear WPF's founder and executive director, Pam Dixon, along with WPF's deputy director and Privacy on the Ground host and producer Kate Kaye, discuss what led to this work and how AI Governance Tools have evolved. This episode of Privacy on the Ground features music by Maciej Sadowski. The Privacy on the Ground intro theme features music by Pangal.

Emotion recognition is baked into all sorts of software and systems many of us use or experience every day, from video call systems measuring the "mood" at a work meeting, to systems used to gauge distraction at school, or impairment or anger of drivers inside their cars. Despite its increasing proliferation, emotion recognition systems and the data use embedded in them create significant privacy impacts.  What is emotion recognition? Would fixing inaccuracy problems in these systems alleviate the potential harms they enable? Should emotion related data be recognized as a sensitive type of information along with health financial and other sensitive data? How might policymakers address potential harms of emotion recognition? Dr. Nazanin Andalibi, Assistant Professor at the University of Michigan School of Information, has a lot to say about all this, and she has the research to back it up. World Privacy Forum Deputy Director Kate Kaye interviewed Dr. Andalibi in June 2024 in Rio de Janeiro, Brazil. This episode of Privacy on the Ground features music by Old Wave. The Privacy on the Ground intro theme features music by Pangal.