Existing research mostly improves the fairness of Machine Learning (ML) software regarding a single protected attribute at a time, but this is unrealistic given that many users have multiple protected attributes. This paper conducts an extensive study of fairness improvement regarding multiple protected attributes, covering 11 state-of-the-art fairness improvement methods. We analyze the effectiveness of these methods with different datasets, metrics, and ML models when considering multiple protected attributes. The results reveal that improving fairness for a single protected attribute can largely decrease fairness regarding unconsidered protected attributes. This decrease is observed in up to 88.3% of scenarios (57.5% on average). More surprisingly, we find little difference in accuracy loss when considering single and multiple protected attributes, indicating that accuracy can be maintained in the multiple-attribute paradigm. However, the effect on precision and recall when handling multiple protected attributes is about 5 times and 8 times that of a single attribute. This has important implications for future fairness research: reporting only accuracy as the ML performance metric, which is currently common in the literature, is inadequate.

Generated texts from large language models (LLMs) are remarkably close to high-quality human-authored text, raising concerns about their potential misuse in spreading false information and academic misconduct. Consequently, there is an urgent need for a highly practical detection tool capable of accurately identifying the source of a given text. However, existing detection tools typically rely on access to LLMs and can only differentiate between machine-generated and human-authored text, failing to meet the requirements of fine-grained tracing, intermediary judgment, and rapid detection. Therefore, we propose LLMDet, a model-specific, secure, efficient, and extendable detection tool, that can source text from specific LLMs, such as GPT-2, OPT, LLaMA, and others. In LLMDet, we record the next-token probabilities of salient n-grams as features to calculate proxy perplexity for each LLM. By jointly analyzing the proxy perplexities of LLMs, we can determine the source of the generated text. Experimental results show that LLMDet yields impressive detection performance while ensuring speed and security, achieving 98.54% precision and x5.0 faster for recognizing human-authored text. Additionally, LLMDet can effortlessly extend its detection capabilities to a new open-source model. We will provide an open-source tool at https://github.com/TrustedLLM/LLMDet.

Today, governments and regions are taking a piecemeal approach, with the E.U. and China moving the fastest toward heavier handed regulation. Seeking to cultivate the sector even as they warn of AI's grave risks, the British have staked out the lightest touch on rules, calling their strategy a "pro innovation" approach. The United States -- home to the largest and most sophisticated AI developers -- is somewhere in the middle, placing new safety obligations on developers of the most sophisticated AI systems but not so much as to stymie development and growth.

In a bustling restaurant in downtown Anytown, USA, an overwhelmed manager turns to AI to help with staff shortages and customer service. Across town, a harried newspaper publisher leverages AI to help generate news content. Both are part of a growing number who rely on AI for everyday business needs. But what happens when the technology errs, or worse, poses risks we haven't fully considered? The current policy conversation is heavily geared toward the eight or so powerful companies that make AI.

Oscar-nominated actor Scarlett Johansson has taken legal action against an AI app developer for using her voice and image in an ad without permission, Variety has reported. The 22-second ad promoted an AI image editor called Lisa AI: 90s Yearbook & Avatar, and reportedly used an AI-generated version of Johansson's voice and image. The ad showed a real clip of Johansson in a Black Widow behind-the-scenes clip, saying "What's up guys? It's Scarlett and I want you to come with me...". It then transitions to AI-generated photos and a cloned version of her voice promoting the AI app.

Large Language Models (LLMs), such as ChatGPT, have drawn a lot of attentions recently in the legal domain due to its emergent ability to tackle a variety of legal tasks. However, it is still unknown if LLMs are able to analyze a legal case and perform reasoning in the same manner as lawyers. Therefore, we constructed a novel corpus consisting of scenarios pertain to Contract Acts Malaysia and Australian Social Act for Dependent Child. ChatGPT is applied to perform analysis on the corpus using the IRAC method, which is a framework widely used by legal professionals for organizing legal analysis. Each scenario in the corpus is annotated with a complete IRAC analysis in a semi-structured format so that both machines and legal professionals are able to interpret and understand the annotations. In addition, we conducted the first empirical assessment of ChatGPT for IRAC analysis in order to understand how well it aligns with the analysis of legal professionals. Our experimental results shed lights on possible future research directions to improve alignments between LLMs and legal experts in terms of legal reasoning.

With the increasing popularity of machine learning models for high-stakes decision-making, it has become a consensus that (1) these models carry implicit biases [1, 2] and (2) these biases should be addressed to improve the fairness of algorithmic decisions [3, 4]. The disparate treatment of such models towards African Americans and women has been illustrated in the well-documented COMPAS [1] and Apple credit card [2] cases. While there has been extensive research on fair algorithms and fair machine learning in recent years, the proposed solutions have mostly disregarded important challenges that arise in real-world settings. Existing work in fair machine learning has focused on tabular, image, and text data [5, 6]. However, in several applications, data can be naturally modeled as graphs (or networks), representing different objects, their relationships, and attributes [7, 8]. Graph Neural Networks (GNNs) have achieved state-of-the-art results in many graph machine learning tasks, including node classification, link prediction, and graph classification [9, 10, 11, 12]. For instance, in the case of link prediction in a professional network, such as LinkedIn, which is a key recruiting and networking tool, link recommendations should not be biased against protected groups [13, 14, 15, 16, 17]. However, guaranteeing fairness in graph data is a challenge due to well-known correlations in the network caused by homophily and influence.

We analyze the structure of the market for foundation models, i.e., large AI models such as those that power ChatGPT and that are adaptable to downstream uses, and we examine the implications for competition policy and regulation. We observe that the most capable models will have a tendency towards natural monopoly and may have potentially vast markets. This calls for a two-pronged regulatory response: (i) Antitrust authorities need to ensure the contestability of the market by tackling strategic behavior, in particular by ensuring that monopolies do not propagate vertically to downstream uses, and (ii) given the diminished potential for market discipline, there is a role for regulators to ensure that the most capable models meet sufficient quality standards (including safety, privacy, non-discrimination, reliability and interoperability standards) to maximally contribute to social welfare. Regulators should also ensure a level regulatory playing field between AI and non-AI applications in all sectors of the economy. For models that are behind the frontier, we expect competition to be quite intense, implying a more limited role for competition policy, although a role for regulation remains.

Topic modeling is a well-established technique for exploring text corpora. Conventional topic models (e.g., LDA) represent topics as bags of words that often require "reading the tea leaves" to interpret; additionally, they offer users minimal semantic control over topics. To tackle these issues, we introduce TopicGPT, a prompt-based framework that uses large language models (LLMs) to uncover latent topics within a provided text collection. TopicGPT produces topics that align better with human categorizations compared to competing methods: for example, it achieves a harmonic mean purity of 0.74 against human-annotated Wikipedia topics compared to 0.64 for the strongest baseline. Its topics are also more interpretable, dispensing with ambiguous bags of words in favor of topics with natural language labels and associated free-form descriptions. Moreover, the framework is highly adaptable, allowing users to specify constraints and modify topics without the need for model retraining. TopicGPT can be further extended to hierarchical topical modeling, enabling users to explore topics at various levels of granularity. By streamlining access to high-quality and interpretable topics, TopicGPT represents a compelling, human-centered approach to topic modeling.

Formal methods refer to rigorous, mathematical approaches to system development and have played a key role in establishing the correctness of safety-critical systems. The main building blocks of formal methods are models and specifications, which are analogous to behaviors and requirements in system design and give us the means to verify and synthesize system behaviors with formal guarantees. This monograph provides a survey of the current state of the art on applications of formal methods in the autonomous systems domain. We consider correct-by-construction synthesis under various formulations, including closed systems, reactive, and probabilistic settings. Beyond synthesizing systems in known environments, we address the concept of uncertainty and bound the behavior of systems that employ learning using formal methods. Further, we examine the synthesis of systems with monitoring, a mitigation technique for ensuring that once a system deviates from expected behavior, it knows a way of returning to normalcy. We also show how to overcome some limitations of formal methods themselves with learning. We conclude with future directions for formal methods in reinforcement learning, uncertainty, privacy, explainability of formal methods, and regulation and certification.