Potential harms of Large Language Models such as mass misinformation and plagiarism can be partially mitigated if there exists a reliable way to detect machine generated text. In this paper, we propose a new watermarking method to detect machine-generated texts. Our method embeds a unique pattern within the generated text, ensuring that while the content remains coherent and natural to human readers, it carries distinct markers that can be identified algorithmically. Specifically, we intervene with the token sampling process in a way which enables us to trace back our token choices during the detection phase. We show how watermarking affects textual quality and compare our proposed method with a state-of-the-art watermarking method in terms of robustness and detectability. Through extensive experiments, we demonstrate the effectiveness of our watermarking scheme in distinguishing between watermarked and non-watermarked text, achieving high detection rates while maintaining textual quality.

Self-supervised speech representations are known to encode both In this work, we explicitly investigate how speaker and speaker and phonetic information, but how they are distributed phonetic information are distributed in the representation space in the high-dimensional space remains largely unexplored. We learned by SSL models. We hypothesize that a good representation hypothesize that they are encoded in orthogonal subspaces, a (one that is efficient and works well for predicting speech) property that lends itself to simple disentanglement. Applying should implicitly disentangle these two sources of information, principal component analysis to representations of two predictive since they vary independently in the processes that generate the coding models, we identify two subspaces that capture speaker speech signal. If so, then the two types of information would be and phonetic variances, and confirm that they are nearly orthogonal.

Algorithmic fairness, the research field of making machine learning (ML) algorithms fair, is an established area in ML. As ML technologies expand their application domains, including ones with high societal impact, it becomes essential to take fairness into consideration during the building of ML systems. Yet, despite its wide range of socially sensitive applications, most work treats the issue of algorithmic bias as an intrinsic property of supervised learning, i.e., the class label is given as a precondition. Unlike prior studies in fairness, we propose an individual fairness measure and a corresponding algorithm that deal with the challenges of uncertainty arising from censorship in class labels, while enforcing similar individuals to be treated similarly from a ranking perspective, free of the Lipschitz condition in the conventional individual fairness definition. We argue that this perspective represents a more realistic model of fairness research for real-world application deployment and show how learning with such a relaxed precondition draws new insights that better explains algorithmic fairness. We conducted experiments on four real-world datasets to evaluate our proposed method compared to other fairness models, demonstrating its superiority in minimizing discrimination while maintaining predictive performance with uncertainty present.

One of the important types of data in several applications is censored survival data processed in the framework of survival analysis [1, 2]. This type of data can be found in applications where objects are characterized by times to some events of interest, for example, by times to failure in reliability, times to recovery or times to death in medicine, times to bankruptcy of a bank or times to an economic crisis in economics. The important peculiarity of survival data is that the corresponding event does not necessarily occur during its observation period. In this case, we say about the so-called censored or right-censored data [3]. There are many machine learning models dealing with survival data, including models based on applying and extending the Cox proportional hazard model [4], for example, models presented in [5, 6], models based on a survival modification of random forests and called random survival forests (RSF) [7, 8, 9, 10, 11], models extending the neural networks [6, 12, 13, 14]. These models have gained considerable attention for their ability to analyze time-to-event data and to predict survival outcomes accurately. However, most models are perceived as black boxes, lacking interpretability.

The string of Autopilot crashes reveals the consequences of allowing a rapidly evolving technology to operate on the nation's roadways without significant government oversight, experts say. While NHTSA has several ongoing investigations into the company and specific crashes, critics argue the agency's approach is too reactive and has allowed a flawed technology to put Tesla drivers -- and those around them -- at risk. The approach contrasts with federal regulation of planes and railroads, where crashes involving new technology or equipment -- such as recurring issues with Boeing's 737 Max -- have resulted in sweeping action by agencies or Congress to ground planes or mandate new safety systems. Unlike planes, which are certified for airworthiness through a process called "type certification," passenger car models are not prescreened, but are subject to a set of regulations called Federal Motor Vehicle Safety Standards, which manufacturers face the burden to meet.

The recently-departed watchdog in charge of monitoring facial recognition technology has joined the private firm he controversially approved, paving the way for the mass roll-out of biometric surveillance cameras in high streets across the country. In a move critics have dubbed an "outrageous conflict of interest", Professor Fraser Sampson, former biometrics and surveillance camera commissioner, has joined Facewatch as a non-executive director. Sampson left his watchdog role on 31 October, with Companies House records showing he was registered as a company director at Facewatch the following day, 1 November. Campaigners claim this might mean he was negotiating his Facewatch contract while in post, and have urged the advisory committee on business appointments to investigate if it may have "compromised his work in public office". It is understood that the committee is currently considering the issue.

Text classification is an important topic in the field of natural language processing. It has been preliminarily applied in information retrieval, digital library, automatic abstracting, text filtering, word semantic discrimination and many other fields. The aim of this research is to use a variety of algorithms to test the ability to identify offensive posts and evaluate their performance against a variety of assessment methods. The motivation for this project is to reduce the harm of these languages to human censors by automating the screening of offending posts. The field is a new one, and despite much interest in the past two years, there has been no focus on the object of the offence. Through the experiment of this project, it should inspire future research on identification methods as well as identification content.

AI-generated media has become a threat to our digital society as we know it. These forgeries can be created automatically and on a large scale based on publicly available technology. Recognizing this challenge, academics and practitioners have proposed a multitude of automatic detection strategies to detect such artificial media. However, in contrast to these technical advances, the human perception of generated media has not been thoroughly studied yet. In this paper, we aim at closing this research gap. We perform the first comprehensive survey into people's ability to detect generated media, spanning three countries (USA, Germany, and China) with 3,002 participants across audio, image, and text media. Our results indicate that state-of-the-art forgeries are almost indistinguishable from "real" media, with the majority of participants simply guessing when asked to rate them as human- or machine-generated. In addition, AI-generated media receive is voted more human like across all media types and all countries. To further understand which factors influence people's ability to detect generated media, we include personal variables, chosen based on a literature review in the domains of deepfake and fake news research. In a regression analysis, we found that generalized trust, cognitive reflection, and self-reported familiarity with deepfakes significantly influence participant's decision across all media categories.

In this paper, we present JADE, a targeted linguistic fuzzing platform which strengthens the linguistic complexity of seed questions to simultaneously and consistently break a wide range of widely-used LLMs categorized in three groups: eight open-sourced Chinese, six commercial Chinese and four commercial English LLMs. JADE generates three safety benchmarks for the three groups of LLMs, which contain unsafe questions that are highly threatening: the questions simultaneously trigger harmful generation of multiple LLMs, with an average unsafe generation ratio of $70\%$ (please see the table below), while are still natural questions, fluent and preserving the core unsafe semantics. We release the benchmark demos generated for commercial English LLMs and open-sourced English LLMs in the following link: https://github.com/whitzard-ai/jade-db. For readers who are interested in evaluating on more questions generated by JADE, please contact us. JADE is based on Noam Chomsky's seminal theory of transformational-generative grammar. Given a seed question with unsafe intention, JADE invokes a sequence of generative and transformational rules to increment the complexity of the syntactic structure of the original question, until the safety guardrail is broken. Our key insight is: Due to the complexity of human language, most of the current best LLMs can hardly recognize the invariant evil from the infinite number of different syntactic structures which form an unbound example space that can never be fully covered. Technically, the generative/transformative rules are constructed by native speakers of the languages, and, once developed, can be used to automatically grow and transform the parse tree of a given question, until the guardrail is broken. For more evaluation results and demo, please check our website: https://whitzard-ai.github.io/jade.html.

Chatbots have been studied for more than half a century. With the rapid development of natural language processing (NLP) technologies in recent years, chatbots using large language models (LLMs) have received much attention nowadays. Compared with traditional ones, modern chatbots are more powerful and have been used in real-world applications. There are however, bias and fairness concerns in modern chatbot design. Due to the huge amounts of training data, extremely large model sizes, and lack of interpretability, bias mitigation and fairness preservation of modern chatbots are challenging. Thus, a comprehensive overview on bias and fairness in chatbot systems is given in this paper. The history of chatbots and their categories are first reviewed. Then, bias sources and potential harms in applications are analyzed. Considerations in designing fair and unbiased chatbot systems are examined. Finally, future research directions are discussed.