With the launch of ChatGPT, Large Language Models (LLMs) are shaking up our whole society, rapidly altering the way we think, create and live. For instance, the GPT integration in Bing has altered our approach to online searching. While nascent LLMs have many advantages, new legal and ethical risks are also emerging, stemming in particular from stochastic parrots and hallucination. The EU is the first and foremost jurisdiction that has focused on the regulation of AI models. However, the risks posed by the new LLMs are likely to be underestimated by the emerging EU regulatory paradigm. Therefore, this correspondence warns that the European AI regulatory paradigm must evolve further to mitigate such risks.

Critical scholarship has elevated the problem of gender bias in data sets used to train virtual assistants (VAs). Most work has focused on explicit biases in language, especially against women, girls, femme-identifying people, and genderqueer folk; implicit associations through word embeddings; and limited models of gender and masculinities, especially toxic masculinities, conflation of sex and gender, and a sex/gender binary framing of the masculine as diametric to the feminine. Yet, we must also interrogate how masculinities are "coded" into language and the assumption of "male" as the linguistic default: implicit masculine biases. To this end, we examined two natural language processing (NLP) data sets. We found that when gendered language was present, so were gender biases and especially masculine biases. Moreover, these biases related in nuanced ways to the NLP context. We offer a new dictionary called AVA that covers ambiguous associations between gendered language and the language of VAs.

The commercial use of Machine Learning (ML) is spreading; at the same time, ML models are becoming more complex and more expensive to train, which makes Intellectual Property Protection (IPP) of trained models a pressing issue. Unlike other domains that can build on a solid understanding of the threats, attacks and defenses available to protect their IP, the ML-related research in this regard is still very fragmented. This is also due to a missing unified view as well as a common taxonomy of these aspects. In this paper, we systematize our findings on IPP in ML, while focusing on threats and attacks identified and defenses proposed at the time of writing. We develop a comprehensive threat model for IP in ML, categorizing attacks and defenses within a unified and consolidated taxonomy, thus bridging research from both the ML and security communities.

Automatic hate speech detection is an important yet complex task, requiring knowledge of common sense, stereotypes of protected groups, and histories of discrimination, each of which may constantly evolve. In this paper, we propose a group-specific approach to NLP for online hate speech detection. The approach consists of creating and infusing historical and linguistic knowledge about a particular protected group into hate speech detection models, analyzing historical data about discrimination against a protected group to better predict spikes in hate speech against that group, and critically evaluating hate speech detection models through lenses of intersectionality and ethics. We demonstrate this approach through a case study on NLP for detection of antisemitic hate speech. The case study synthesizes the current English-language literature on NLP for antisemitism detection, introduces a novel knowledge graph of antisemitic history and language from the 20th century to the present, infuses information from the knowledge graph into a set of tweets over Logistic Regression and uncased DistilBERT baselines, and suggests that incorporating context from the knowledge graph can help models pick up subtle stereotypes.

The 2023 Ford Mustang Mach-E is equipped with the latest semi-autonomous BlueCruise highway driving system that can drive the car under certain circumstances better than the original version. The family of Formula One star Michael Schumacher is planning to take legal action against a German weekly magazine after it published an "interview" with the racer that was generated using artificial intelligence. A spokesperson for the Schumacher family pointed to published reports of legal action, according to Reuters. Family spokesperson Sabine Kehm confirmed to The Associated Press by email on Thursday that legal action is being planned over a "fake artificial intelligence interview by German outlet Die Aktuelle." Schumacher has not been seen in public since suffering a near-fatal brain injury in a skiing accident on a French Alps vacation in December 2013.

The AI chatbot ChatGPT produces content that can appear to have been created by a human. There are many proposed uses for the technology, but its impressive capabilities raise important questions about ownership of the content. UK legislation has a definition for computer-generated works. The first question is whether ChatGPT should be allowed to use original content generated by third parties to generate its responses. The second is whether only humans can be credited as the authors of AI-generated content, or whether the AI itself can be regarded as an author – particularly when that output is creative. Let's deal with question one.

The European Union is in the final stages of crafting first-of-its-kind legislation to regulate harmful uses of artificial intelligence. However, as it currently stands, the proposed law, called the EU AI Act, contains a lethal blind spot: It does not ban the many harmful and dangerous uses of AI systems in the context of immigration enforcement. We, a coalition of human rights organisations, call on EU lawmakers to make sure that this landmark legislation protects everyone, including asylum seekers and others on the move at Europe's borders from dangerous and racist surveillance technologies. We call on them to ensure AI technologies are used to #ProtectNotSurveil. Europe's borders are becoming deadlier with each passing day.

Fox News contributor Joe Concha joins'Fox & Friends First' to discuss Elon Musk's warning that artificial intelligence could threaten elections and his concerns on the declining birth rate. A government watchdog group Tuesday requested that Missouri Attorney General Andrew Bailey investigate a tax-payer-funded Missouri State University business boot program that excluded White males. In a letter to Bailey's office, the Equal Protection Project (EPP) alleged that Missouri State University (MSU) was "engaging in racial- and gender-based discrimination through its sponsorship, promotion, and hosting of a small business training'boot camp' that limits participation" to women and people who identify as "BIPOC" – an acronym for non-white "Black, Indigenous and Persons of Color." MSU began accepting applications for the Spring 2023 Early-Stage Business Boot Camp program in late November. The university said the program was for "aspiring or current BIPOC and/or women small business owners who have recently started or are in the idea phase" and live in Southern Missouri.

With the rapid popularity of large language models such as ChatGPT and GPT-4, a growing amount of attention is paid to their safety concerns. These models may generate insulting and discriminatory content, reflect incorrect social values, and may be used for malicious purposes such as fraud and dissemination of misleading information. Evaluating and enhancing their safety is particularly essential for the wide application of large language models (LLMs). To further promote the safe deployment of LLMs, we develop a Chinese LLM safety assessment benchmark. Our benchmark explores the comprehensive safety performance of LLMs from two perspectives: 8 kinds of typical safety scenarios and 6 types of more challenging instruction attacks. Our benchmark is based on a straightforward process in which it provides the test prompts and evaluates the safety of the generated responses from the evaluated model. In evaluation, we utilize the LLM's strong evaluation ability and develop it as a safety evaluator by prompting. On top of this benchmark, we conduct safety assessments and analyze 15 LLMs including the OpenAI GPT series and other well-known Chinese LLMs, where we observe some interesting findings. For example, we find that instruction attacks are more likely to expose safety issues of all LLMs. Moreover, to promote the development and deployment of safe, responsible, and ethical AI, we publicly release SafetyPrompts including 100k augmented prompts and responses by LLMs.

People form judgments and make decisions based on the information that they observe. A growing portion of that information is not only provided, but carefully curated by social media platforms. Although lawmakers largely agree that platforms should not operate without any oversight, there is little consensus on how to regulate social media. There is consensus, however, that creating a strict, global standard of "acceptable" content is untenable (e.g., in the US, it is incompatible with Section 230 of the Communications Decency Act and the First Amendment). In this work, we propose that algorithmic filtering should be regulated with respect to a flexible, user-driven baseline. We provide a concrete framework for regulating and auditing a social media platform according to such a baseline. In particular, we introduce the notion of a baseline feed: the content that a user would see without filtering (e.g., on Twitter, this could be the chronological timeline). We require that the feeds a platform filters contain "similar" informational content as their respective baseline feeds, and we design a principled way to measure similarity. This approach is motivated by related suggestions that regulations should increase user agency. We present an auditing procedure that checks whether a platform honors this requirement. Notably, the audit needs only black-box access to a platform's filtering algorithm, and it does not access or infer private user information. We provide theoretical guarantees on the strength of the audit. We further show that requiring closeness between filtered and baseline feeds does not impose a large performance cost, nor does it create echo chambers.