Issue #5 of the AI Safety & Governance Newsletter
Guidance reports by NIST on generative AI, enforcing voluntary compliance to pre-release safety testing of AI models, UN AI governance principles, AI's impact on perceptions of linguistic authenticity
Welcome to the 5th issue of the Artificial Intelligence (AI) Safety & Governance Newsletter. Thank you for subscribing! Let’s dive right in.
Key Reads
When a new technology emerges, there are opportunities and risks. Generative AI is no different. The National Institute of Standards and Technology (NIST) published a report describing a set of 12 distinct risks for the widespread use of generative AI technology, including the spread of misinformation, data privacy violations, and the potential for AI to generate harmful content or biased outputs (link).
The report is one of four “guidance” reports published by NIST. Another report focuses on guidelines for documenting the origin and history of content, using methods like digital watermarking and recording metadata (link). The third advocates for collaborative efforts with international partners to develop and implement AI-related consensus standards (link), and the final report is a specialized extension of the Secure Software Development Framework (SSDF) (link) tailored specifically for generative AI and foundation model development. This extension provides a set of augmented recommendations and considerations that align with established SSDF practices (link).
See Tech Policy for a summary of each report (link).
A new bill introduced by U.S. Senators Mark R. Warner and Thom Tillis, co-chairs of the Senate Cybersecurity Caucus, aims to improve the tracking and processing of security and safety incidents and risks associated with AI, including enhancing information sharing between the federal government and private companies (link, link).
A new Florida law mandates that political advertisements using generative AI to depict unreal events prominently display a disclaimer, with violations treated as first-degree misdemeanors, effective July 1, 2024 (link).
Jeremy Howard, CEO of Answer.AI, critiques California’s SB-1047 (link), arguing it could stifle AI innovation by imposing harsh regulations on open-source development and small businesses. He suggests focusing on regulating AI applications in high-risk areas rather than development (link).
A new Colorado law aims to protect the privacy of neural data, increasingly collected by consumer neuro-technologies (link).
Tina Stowell, a member of the House of Lords in the UK Parliament, expressed approval for the UK government’s commitment to AI safety and support for startups but criticized its handling of copyright issues and lack of clear policies on AI standards. She emphasized the need for more decisive actions to protect the integrity of British businesses and innovation in a recent letter. See longer discussion in The Register (link).
The non-profit privacy group noyb, led by activist Max Schrems, has filed a complaint with the Austrian data protection authority accusing OpenAI’s ChatGPT of violating the EU's General Data Protection Regulation (GDPR). The complaint centers on ChatGPT’s generation of inaccurate personal data, specifically incorrect birthdays, and its failure to correct or delete such data upon request (link). This issue is part of broader concerns in Europe, as evidenced by Italy’s temporary ban on ChatGPT and the formation of a task force by the European Data Protection Board to address these challenges.
At the AI safety summit at Bletchley Park, technology companies initially agreed to allow the UK’s AI Safety Institute (AISI) to conduct pre-release safety testing on new AI models. However, six months later, this agreement appears to have largely failed to materialize, according to a new article by Vincent Manancourt, Gian Volpicelli, and Mohar Chatterjee for Politico (link). Despite the UK government’s claims of commencing such testing, most new AI models, like Meta’s Llama-3, have not been subjected to pre-release checks by the AISI. This situation highlights the challenges of enforcing voluntary compliance in the absence of stringent legal requirements and the reluctance of AI firms to share sensitive technologies due to fears of competition and jurisdictional issues.
Nonprofits backed by technology billionaires, such as the Center for AI Policy and Center for AI Safety, have begun lobbying in Washington to address the existential risks of AI, writes Brendan Bordelon for Politico (link). According to Brendan, these nonprofits advocate for regulations that could impose severe liabilities on AI developers and potentially halt high-risk AI projects. However, critics argue that these efforts might stifle smaller AI companies by raising entry barriers and focusing too much on catastrophic scenarios.
The use of AI to create compelling propaganda is a key risk. A recent survey experiment conducted with US participants examined the persuasiveness of news articles produced by foreign propagandists compared to content generated by a large language model (GPT-3) (link). The study found that both the original propaganda articles and those generated by GPT-3 were highly persuasive. While GPT-3-generated content was slightly less persuasive on average than the original propaganda, human involvement through editing prompts and curating output improved its persuasiveness.
During the recent 2024 China-Africa Internet Development and Cooperation Forum, collaboration between African countries and China on AI governance was one of the key themes. The call to action includes strengthening policy dialogue, promoting technology research and application, fostering industrial cooperation, enhancing talent exchanges and capacity building, and bolstering safeguards for cyber and data security (link).
An article by Alex Hern for The Guardian discusses the emergence of distinct linguistic patterns, dubbed “AI-ese”, in responses generated by AI chatbots (link). Alex highlights the telltale signs of AI-generated content, such as exaggerated politeness and aversions to brevity, alongside idiosyncratic linguistic tendencies like the overuse of words like “delve” (a recent source of controversy when a famous venture capitalist, Paul Graham, alluded to ignoring a message that included the word “delve”).
Labelling for AI chatbots, including large language models is often outsourced to cheaper labor markets. This implies their linguistic patterns can mimic those of the labelers. For instance, the overuse of “delve” in ChatGPT’s responses may reflect the linguistic norms of Nigerian English, where the word is more prevalent. Alex’s article underscores how the blurred lines between AI-generated content and human speech pose implications for language dynamics and societal perceptions of linguistic authenticity. Shout out to my senior colleague Kush Varshney for sending this article my way.
A new analysis by the Brookings Institute shows a 1500% increase in the potential value of US Department of Defense contracts related to AI between August 2022 and August 2023 (link).
Belinda Cleeland, Maxime Stauffer, and Malou Estier provide a summary and review of the Global Digital Compact (GDC) Zero Draft. The Zero Draft of the GDC is a set of principles and proposed actions for global governance of emerging technologies, particularly AI, set to be adopted by member states of the United Nations (link).
Opportunities
A new program by NIST called the GenAI evaluation program is seeking approaches from academia and industry on ways to discriminate between synthetic and human-generated content in text-to-text and text-to-image modalities. Registration is currently open (link).
Can wisdom and philosophy be automated? AI Impacts announced an essay competition focusing on the automation of wisdom and philosophy. The aim is to explore the potential impact of advanced AI on decision-making processes and philosophical understanding. There is a total prize pool of $25,000 (link).
Thank you for reading!
Victor.