AI-generated images have become so good in recent years that individuals cannot distinguish them any more from "real" images. This development creates a series of societal risks, and challenges our perception of what is true and what is not, particularly with the emergence of "deep fakes" that impersonate real individuals. Watermarking, a technique that involves embedding identifying information within images to indicate their AI-generated nature, has emerged as a primary mechanism to address the risks posed by AI-generated images. The implementation of watermarking techniques is now becoming a legal requirement in many jurisdictions, including under the new 2024 EU AI Act. Despite the widespread use of AI image generation systems, the current status of watermarking implementation remains largely unexamined. Moreover, the practical implications of the AI Act's watermarking requirements have not previously been studied. The present paper therefore both provides an empirical analysis of 50 of the most widely used AI systems for image generation, and embeds this empirical analysis into a legal analysis of the AI Act. We identify four categories of generative AI image systems relevant under the AI Act, outline the legal obligations for each category, and find that only a minority number of providers currently implement adequate watermarking practices.

Ensuring transparency of data practices related to personal information is a fundamental requirement under the General Data Protection Regulation (GDPR), particularly as mandated by Articles 13 and 14. However, assessing compliance at scale remains a challenge due to the complexity and variability of privacy policy language. Manual audits are resource-intensive and inconsistent, while existing automated approaches lack the granularity needed to capture nuanced transparency disclosures. In this paper, we introduce a large language model (LLM)-based framework for word-level GDPR transparency compliance annotation. Our approach comprises a two-stage annotation pipeline that combines initial LLM-based annotation with a self-correction mechanism for iterative refinement. This annotation pipeline enables the systematic identification and fine-grained annotation of transparency-related content in privacy policies, aligning with 21 GDPR-derived transparency requirements. To enable large-scale analysis, we compile a dataset of 703,791 English-language policies, from which we generate a sample of 200 manually annotated privacy policies. To evaluate our approach, we introduce a two-tiered methodology assessing both label- and span-level annotation performance. We conduct a comparative analysis of eight high-profile LLMs, providing insights into their effectiveness in identifying GDPR transparency disclosures. Our findings contribute to advancing the automation of GDPR compliance assessments and provide valuable resources for future research in privacy policy analysis.

Foundation models are critical digital technologies with sweeping societal impact that necessitates transparency. To codify how foundation model developers should provide transparency about the development and deployment of their models, we propose Foundation Model Transparency Reports, drawing upon the transparency reporting practices in social media. While external documentation of societal harms prompted social media transparency reports, our objective is to institutionalize transparency reporting for foundation models while the industry is still nascent. To design our reports, we identify 6 design principles given the successes and shortcomings of social media transparency reporting. To further schematize our reports, we draw upon the 100 transparency indicators from the Foundation Model Transparency Index. Given these indicators, we measure the extent to which they overlap with the transparency requirements included in six prominent government policies (e.g., the EU AI Act, the US Executive Order on Safe, Secure, and Trustworthy AI). Well-designed transparency reports could reduce compliance costs, in part due to overlapping regulatory requirements across different jurisdictions. We encourage foundation model developers to regularly publish transparency reports, building upon recommendations from the G7 and the White House.

The European Union has proposed the Artificial Intelligence Act which introduces detailed requirements of transparency for AI systems. Many of these requirements can be addressed by the field of explainable AI (XAI), however, there is a fundamental difference between XAI and the Act regarding what transparency is. The Act views transparency as a means that supports wider values, such as accountability, human rights, and sustainable innovation. In contrast, XAI views transparency narrowly as an end in itself, focusing on explaining complex algorithmic properties without considering the socio-technical context. We call this difference the ``transparency gap''. Failing to address the transparency gap, XAI risks leaving a range of transparency issues unaddressed. To begin to bridge this gap, we overview and clarify the terminology of how XAI and European regulation -- the Act and the related General Data Protection Regulation (GDPR) -- view basic definitions of transparency. By comparing the disparate views of XAI and regulation, we arrive at four axes where practical work could bridge the transparency gap: defining the scope of transparency, clarifying the legal status of XAI, addressing issues with conformity assessment, and building explainability for datasets.

The European Union's flagship artificial intelligence regulation took a major step toward becoming law on Wednesday, after lawmakers voted to approve the text of the law that would ban real-time facial recognition, and place new transparency requirements on generative AI tools like ChatGPT. AI Act--will now progress to the final "trilogue" stage of the E.U.'s regulatory process. There, officials will attempt to reach a compromise between the draft of the law just approved by the E.U. Parliament, a different version preferred by the bloc's executive branch, and the desires of member states. That process will begin on Wednesday night and must be completed by January if the law is to come into force before E.U. elections next year.

The publication of this series was inspired by the release ChatGPT, which is a generative artificial intelligence (AI) chatbox developed by Open AI. ChatGPT uses machine learning and natural language processing to provide relatively sophisticated and human-like responses to almost any question. Unlike traditional AI systems, ChatGPT is a generative AI platform, which means that the content it creates is "new," rather than a reiteration of something that already exists. As ChatGPT demonstrates, content can be produced through generative AI in a matter of seconds and may be composed of images, videos, audio, text or even code. The reality is that generative AI is well on the way to becoming not just faster and cheaper, but better in some cases than what humans create by hand.

We have entered the brave new world of AI chatbots. This means everything from reenvisioning how students learn in school to protecting ourselves from mass-produced misinformation. It also means heeding the mounting calls to regulate AI to help us navigate an era in which computers write as fluently as people. So far, there is more agreement on the need for AI regulation than on what this would entail. Mira Murati, head of the team that created the chatbot app ChatGPT--the fastest growing consumer-Internet app in history--said governments and regulators should be involved, but she didn't suggest how.

The Artificial Intelligence Act was introduced to the European Union in April 2021, and is rapidly progressing through comment periods and rewrites. When it goes into effect, which experts say could occur at the beginning of 2023, it will have a broad impact on the use of AI and machine learning for citizens and companies around the world. The AI law aims to create a common regulatory and legal framework for the use of AI, including how it's developed, what companies can use it for, and the legal consequences of failing to adhere to the requirements. The law will likely require companies to receive approval before adopting AI for some use cases, outlaw certain other AI uses deemed too risky, and create a public list of other high-risk AI uses. At a broad level, the law seeks to codify the EU's trustworthy AI paradigm, according to an official presentation on the law by the European Commission, the continent's governing body.

There are some broad principles in the Report that should be included in the upcoming regulation, such as, for example, the principle that AI should respect human agency and democratic oversight. The Report also offers recommendations related to transparency, such as a proposed requirement that developers and deployers of high-risk technologies provide documentation to public authorities on their use and design and, "when strictly necessary," "source code, development tools and data used by the system". However, these measures fall short of the transparency requirements which civil society have called for, including the joint recommendation of Access Now and AlgorithmWatch to establish public registers for AI systems used in the public sector. Moreover, any transparency requirements that focus only on allegedly "high-risk" systems leave a gap in oversight of systems that do not fall into this category but still threaten our rights.

Between 2018 and 2019, organizations that have deployed artificial intelligence (AI) grew from 4% to 14%, according to Gartner's 2019 CIO Agenda survey. AI is reaching organizations in many different ways compared with a few years ago, when there was no alternative to building your own solutions with machine learning (ML). AutoML and intelligent applications have the greatest momentum, while other approaches are also popular -- namely, AI platform as a service or AI cloud services. Conversational AI remains at the top of corporate agendas spurred by the worldwide success of Amazon Alexa, Google Assistant and others. Meanwhile, new technologies continue to emerge such as augmented intelligence, edge AI, data labeling and explainable AI.