Silverman has filed the suits along with two authors, Christopher Golden and Richard Kadrey, in which they claim the AI models developed by OpenAI and Meta used their work as part of their training data. Tools like ChatGPT, a highly popular chatbot, are based on large language models that are fed vast amounts of data taken from the internet in order to train them to give convincing responses to text prompts from users. The suits claim the authors' works were obtained from "shadow library" sites that have "long been of interest to the AI-training community". The OpenAI suit includes exhibits claiming that, when prompted, it summarised three books: Silverman's The Bedwetter, Ararat by Golden, and Kadrey's Sandman Slim. The Meta suit cites multiple works by Kadrey and Golden, alongside The Bedwetter, and flags a Meta paper that indicates LLaMA's training datasets included material taken from shadow libraries the suit describes as "flagrantly illegal".

Meta Inc's Threads app launched by Instagram that has been called a Twitter-killer has signed up more than 100 million users in less than five days. That is according to data tracking websites on Monday, suggesting the app has smashed the record of AI tool ChatGPT for fastest-growing consumer app. While ChatGPT took two months to hit the 100 million user mark and video-sharing app TikTok took nine months, Instagram itself took two and a half years to reach that mark after its 2010 launch. Threads went live on Apple and Android app stores in 100 countries late on Wednesday (July 5), though it is not available in Europe because parent company Meta is unsure how to navigate the European Union's data privacy legislation. Meanwhile, experts have described the traffic of Elon Musk-owned Twitter as'tanking' in the face of the new competition.

The Threads app launched by Instagram as a rival to Twitter has seen more than 100 million users sign up in less than five days, data tracking websites said on Monday, smashing the record of artificial intelligence tool ChatGPT for the fastest-growing consumer app. While ChatGPT took two months to hit the 100-million-user mark and video-sharing app TikTok took nine months, Instagram itself took two and a half years to reach the same mark after its 2010 launch. Threads went live on Apple and Android app stores in 100 countries late on Wednesday, though it is not available in Europe due to legal issues the parent company Meta has had with the European Union's data privacy legislation. Twitter is thought to have around 200 million regular users but it has suffered repeated technical failures since Elon Musk bought the platform last year and sacked thousands of staff. Musk, who also serves as the boss of Tesla and SpaceX, has also alienated many users by introducing charges for previously free services and allowing banned right-wing accounts back on the platform.

The use of AI in hiring has been criticized for the way it automates and entrenches existing racial and gender biases. AI systems that evaluate candidates' facial expressions and language have been shown to prioritize white, male, and abled-bodied candidates. The problem is massive, and many companies use AI at least once during the hiring process. US Equal Employment Opportunity Commission chair Charlotte Burrows said in a meeting in January that as many as four out of five companies use automation to make employment decisions. NYC's Automated Employment Decision Tool law, which came into force on Wednesday, says that employers who use AI in hiring have to tell candidates they are doing so.

Assuming that the term 'metaverse' could be understood as a computer-based implementation of multiverse applications, we started to look in the present work for a logic that would be powerful enough to handle the situations arising both in the real and in the fictional underlying application domains. Realizing that first-order logic fails to account for the unstable behavior of even the most simpleminded information system domains, we resorted to non-conventional extensions, in an attempt to sketch a minimal composite logic strategy. The discussion was kept at a rather informal level, always trying to convey the intuition behind the theoretical notions in natural language terms, and appealing to an AI agent, namely ChatGPT, in the hope that algorithmic and common-sense approaches can be usefully combined.

The importance of high data quality is increasing with the growing impact and distribution of ML systems and big data. Also the planned AI Act from the European commission defines challenging legal requirements for data quality especially for the market introduction of safety relevant ML systems. In this paper we introduce a novel approach that supports the data quality assurance process of multiple data quality aspects. This approach enables the verification of quantitative data quality requirements. The concept and benefits are introduced and explained on small example data sets. How the method is applied is demonstrated on the well known MNIST data set based an handwritten digits.

The concentration of power in a few digital technology companies has become a subject of increasing interest in both academic and non-academic discussions. One of the most noteworthy contributions to the debate is Lina Khan's Amazon's Antitrust Paradox. In this work, Khan contends that Amazon has systematically exerted its dominance in online retail to eliminate competitors and subsequently charge above-market prices. This work contributed to Khan's appointment as the chair of the US Federal Trade Commission (FTC), one of the most influential antitrust organisations. Today, several ongoing antitrust lawsuits in the US and Europe involve major technology companies like Apple, Google/Alphabet, and Facebook/Meta. In the realm of generative AI, we are once again witnessing the same companies taking the lead in technological advancements, leaving little room for others to compete. This article examines the market dominance of these corporations in the technology stack behind generative AI from an antitrust law perspective.

Recent work on Event Extraction has reframed the task as Question Answering (QA), with promising results. The advantage of this approach is that it addresses the error propagation issue found in traditional token-based classification approaches by directly predicting event arguments without extracting candidates first. However, the questions are typically based on fixed templates and they rarely leverage contextual information such as relevant arguments. In addition, prior QA-based approaches have difficulty handling cases where there are multiple arguments for the same role. In this paper, we propose QGA-EE, which enables a Question Generation (QG) model to generate questions that incorporate rich contextual information instead of using fixed templates. We also propose dynamic templates to assist the training of QG model. Experiments show that QGA-EE outperforms all prior single-task-based models on the ACE05 English dataset.

Online communities of involuntary celibates (incels) are a prominent source of misogynist hate speech. In this paper, we use quantitative text and network analysis approaches to examine how identity groups are discussed on incels-dot-is, the largest black-pilled incels forum. We find that this community produces a wide range of novel identity terms and, while terms for women are most common, mentions of other minoritized identities are increasing. An analysis of the associations made with identity groups suggests an essentialist ideology where physical appearance, as well as gender and racial hierarchies, determine human value. We discuss implications for research into automated misogynist hate speech detection.

Motivated by a plethora of practical examples where bias is induced by automated-decision making algorithms, there has been strong recent interest in the design of fair algorithms. However, there is often a dichotomy between fairness and efficacy: fair algorithms may proffer low social welfare solutions whereas welfare optimizing algorithms may be very unfair. This issue is exemplified in the machine scheduling problem where, for $n$ jobs, the social welfare of any fair solution may be a factor $\Omega(n)$ worse than the optimal welfare. In this paper, we prove that this dichotomy between fairness and efficacy can be overcome if we allow for a negligible amount of bias: there exist algorithms that are both "almost perfectly fair" and have a constant factor efficacy ratio, that is, are guaranteed to output solutions that have social welfare within a constant factor of optimal welfare. Specifically, for any $\epsilon>0$, there exist mechanisms with efficacy ratio $\Theta(\frac{1}{\epsilon})$ and where no agent is more than an $\epsilon$ fraction worse off than they are in the fairest possible solution (given by an algorithm that does not use personal or type data). Moreover, these bicriteria guarantees are tight and apply to both the single machine case and the multiple machine case. The key to our results are the use of Pareto scheduling mechanisms. These mechanisms, by the judicious use of personal or type data, are able to exploit Pareto improvements that benefit every individual; such Pareto improvements would typically be forbidden by fair scheduling algorithms designed to satisfy standard statistical measures of group fairness. We anticipate this paradigm, the judicious use of personal data by a fair algorithm to greatly improve performance at the cost of negligible bias, has wider application.