As AI systems are increasingly incorporated into domains where human behavior has set the norm, a challenge for AI governance and AI alignment research is to regulate their behavior in a way that is useful and constructive for society. One way to answer this question is to ask: how do we govern the human behavior that the models are emulating? To evaluate human behavior, the American legal system often uses the "Reasonable Person Standard." The idea of "reasonable" behavior comes up in nearly every area of law. The legal system often judges the actions of parties with respect to what a reasonable person would have done under similar circumstances. This paper argues that the reasonable person standard provides useful guidelines for the type of behavior we should develop, probe, and stress-test in models. It explains how reasonableness is defined and used in key areas of the law using illustrative cases, how the reasonable person standard could apply to AI behavior in each of these areas and contexts, and how our societal understanding of "reasonable" behavior provides useful technical goals for AI researchers.

Recent improvements in large language models (LLMs) have significantly enhanced natural language processing (NLP) applications. However, these models can also inherit and perpetuate biases from their training data. Addressing this issue is crucial, yet many existing datasets do not offer evaluation across diverse NLP tasks. To tackle this, we introduce the Bias Evaluations Across Domains (BEADs) dataset, designed to support a wide range of NLP tasks, including text classification, bias entity recognition, bias quantification, and benign language generation. BEADs uses AI-driven annotation combined with experts' verification to provide reliable labels. This method overcomes the limitations of existing datasets that typically depend on crowd-sourcing, expert-only annotations with limited bias evaluations, or unverified AI labeling. Our empirical analysis shows that BEADs is effective in detecting and reducing biases across different language models, with smaller models fine-tuned on BEADs often outperforming LLMs in bias classification tasks. However, these models may still exhibit biases towards certain demographics. Fine-tuning LLMs with our benign language data also reduces biases while preserving the models' knowledge. Our findings highlight the importance of comprehensive bias evaluation and the potential of targeted fine-tuning for reducing the bias of LLMs. We are making BEADs publicly available at https://huggingface.co/datasets/shainar/BEAD Warning: This paper contains examples that may be considered offensive.

This is a long paper, an essay, on ambiguity, pragmatics, legal ecosystems, and the expressive function of law. It is divided into two parts and fifteen sections. The first part (Pragmatics) addresses ambiguity from the perspective of linguistic and cognitive pragmatics in the legal field. The second part (Computing) deals with this issue from the point of view of human-centered design and artificial intelligence, specifically focusing on the notion and modelling of rules and what it means to comply with the rules. This is necessary for the scaffolding of smart legal ecosystems (SLE). I will develop this subject with the example of the architecture, information flows, and smart ecosystem of OPTIMAI, an EU project of Industry 4.0 for zero-defect manufacturing (Optimizing Manufacturing Processes through Artificial Intelligence and Virtualization).

In recent years, "prompt as a service" has greatly enhanced the utility of large language models (LLMs) by enabling them to perform various downstream tasks efficiently without fine-tuning. This has also increased the commercial value of prompts. However, the potential risk of leakage in these commercialized prompts remains largely underexplored. In this paper, we introduce a novel attack framework, PRSA, designed for prompt stealing attacks against LLMs. The main idea of PRSA is to infer the intent behind a prompt by analyzing its input-output content, enabling the generation of a surrogate prompt that replicates the original's functionality. Specifically, PRSA mainly consists of two key phases: prompt mutation and prompt pruning. In the mutation phase, we propose a prompt attention algorithm based on output difference. The algorithm facilitates the generation of effective surrogate prompts by learning key factors that influence the accurate inference of prompt intent. During the pruning phase, we employ a two-step related word identification strategy to detect and mask words that are highly related to the input, thus improving the generalizability of the surrogate prompts. We verify the actual threat of PRSA through evaluation in both real-world settings, non-interactive and interactive prompt services. The results strongly confirm the PRSA's effectiveness and generalizability. We have reported these findings to prompt service providers and actively collaborate with them to implement defensive measures.

The evolution of video generation from text, starting with animating MNIST numbers to simulating the physical world with Sora, has progressed at a breakneck speed over the past seven years. While often seen as a superficial expansion of the predecessor text-to-image generation model, text-to-video generation models are developed upon carefully engineered constituents. Here, we systematically discuss these elements consisting of but not limited to core building blocks (vision, language, and temporal) and supporting features from the perspective of their contributions to achieving a world model. We employ the PRISMA framework to curate 97 impactful research articles from renowned scientific databases primarily studying video synthesis using text conditions. Upon minute exploration of these manuscripts, we observe that text-to-video generation involves more intricate technologies beyond the plain extension of text-to-image generation. Our additional review into the shortcomings of Sora-generated videos pinpoints the call for more in-depth studies in various enabling aspects of video generation such as dataset, evaluation metric, efficient architecture, and human-controlled generation. Finally, we conclude that the study of the text-to-video generation may still be in its infancy, requiring contribution from the cross-discipline research community towards its advancement as the first step to realize artificial general intelligence (AGI).

The Federal Trade Commission and Justice Department have reached a deal that would set the stage for antitrust probes into Microsoft, OpenAI and Nvidia, setting up unprecedented regulatory scrutiny of the companies' conduct in the AI race, according to a person familiar with the matter, who spoke on the condition of anonymity to discuss a probe whose details are not public.

Microsoft is under investigation by the Federal Trade Commission over its deal with Inflection AI, according to The Wall Street Journal. Back in March, the company hired almost all of Inflection AI's employees, including founders Karén Simonyan and Mustafa Suleyman, who was also a DeepMind cofounder. In addition, Microsoft paid Inflection AI 650 million to license its artificial intelligence technology. Now, the FTC wants to know whether the companies deliberately structured the deal to avoid being the subject of regulatory antitrust review. As The Journal notes, companies are required to report any acquisition that's valued at 119 million or more to federal antitrust agencies.

Until mid-2023, artificial intelligence was something of a niche topic in Washington, largely confined to small circles of tech-policy wonks. That all changed when, nearly two years into Gina Raimondo's tenure as Secretary of Commerce, ChatGPT's explosive popularity catapulted AI into the spotlight. Raimondo, however, was ahead of the curve. "I make it my business to stay on top of all of this," she says during an interview in her wood-paneled office overlooking the National Mall on May 21. "None of it was shocking to me." But in the year since, even she has been startled by the pace of progress.

Microsoft, OpenAI and Nvidia face increased antitrust scrutiny of their roles in the artificial intelligence industry after a report that US regulators have reached an agreement on investigating the companies. The New York Times reported that the US justice department and the Federal Trade Commission (FTC) have reached an agreement on investigations into the main protagonists in the AI market. The deal is expected to be completed in the coming days, according to the report. The justice department will lead on investigating whether Nvidia, the leading maker of chips that train and operate AI systems, has broken antitrust laws that oversee fair competition in business and aim to prevent monopolies, said the NYT on Wednesday. The Wall Street Journal also reported on Thursday that the FTC is investigating whether Microsoft structured a recent deal with startup Inflection AI to avoid an antitrust inquiry.

Large language models (LLMs) are typically prompted to follow a single instruction per inference call. In this work, we analyze whether LLMs also hold the capability to handle multiple instructions simultaneously, denoted as Multi-Task Inference. For this purpose, we introduce the MTI Bench(Multi-Task Inference Benchmark), a comprehensive evaluation benchmark encompassing 5,000 instances across 25 tasks. Each task in the MTI Bench involves 2 to 3 sub-tasks. As expected, we first demonstrate that Multi-Task Inference reduces the total inference time by 1.46 times in average since it does not require multiple inference calls. Interestingly, contrary to the expectation that LLMs would perform better when tasks are divided, we find that state-of-the-art LLMs, such as Llama-2-Chat-70B and GPT-4, show up to 7.3% and 12.4% improved performance with Multi-Task Inference compared to Single-Task Inference on the MTI Bench. We release the MTI Bench dataset and our code at this link https://github.com/guijinSON/MTI-Bench.