Authorship obfuscation techniques hold the promise of helping people protect their privacy in online communications by automatically rewriting text to hide the identity of the original author. However, obfuscation has been evaluated in narrow settings in the NLP literature and has primarily been addressed with superficial edit operations that can lead to unnatural outputs. In this work, we introduce an automatic text privatization framework that fine-tunes a large language model via reinforcement learning to produce rewrites that balance soundness, sense, and privacy. We evaluate it extensively on a large-scale test set of English Reddit posts by 68k authors composed of short-medium length texts. We study how the performance changes among evaluative conditions including authorial profile length and authorship detection strategy. Our method maintains high text quality according to both automated metrics and human evaluation, and successfully evades several automated authorship attacks.

In real-world scenarios, extensive manual annotation for continual learning is impractical due to prohibitive costs. Although prior arts, influenced by large-scale webly supervised training, suggest leveraging web-scraped data in continual learning, this poses challenges such as data imbalance, usage restrictions, and privacy concerns. Addressing the risks of continual webly supervised training, we present an online continual learning framework - Generative Name only Continual Learning (G-NoCL). The proposed G-NoCL uses a set of generators G along with the learner. When encountering new concepts (i.e., classes), G-NoCL employs the novel sample complexity-guided data ensembling technique DIverSity and COmplexity enhancing ensemBlER (DISCOBER) to optimally sample training data from generated data. Through extensive experimentation, we demonstrate superior performance of DISCOBER in G-NoCL online CL benchmarks, covering both In-Distribution (ID) and Out-of-Distribution (OOD) generalization evaluations, compared to naive generator-ensembling, web-supervised, and manually annotated data.

The burgeoning landscape of text-to-image models, exemplified by innovations such as Midjourney and DALLE 3, has revolutionized content creation across diverse sectors. However, these advancements bring forth critical ethical concerns, particularly with the misuse of open-source models to generate content that violates societal norms. Addressing this, we introduce Ethical-Lens, a framework designed to facilitate the value-aligned usage of text-to-image tools without necessitating internal model revision. Ethical-Lens ensures value alignment in text-to-image models across toxicity and bias dimensions by refining user commands and rectifying model outputs. Systematic evaluation metrics, combining GPT4-V, HEIM, and FairFace scores, assess alignment capability. Our experiments reveal that Ethical-Lens enhances alignment capabilities to levels comparable with or superior to commercial models like DALLE 3, ensuring user-generated content adheres to ethical standards while maintaining image quality. This study indicates the potential of Ethical-Lens to ensure the sustainable development of open-source text-to-image tools and their beneficial integration into society. Our code is available at https://github.com/yuzhu-cai/Ethical-Lens.

The integration of artificial intelligence (AI) into daily life, particularly through information retrieval and recommender systems, has necessitated advanced user modeling and profiling techniques to deliver personalized experiences. These techniques aim to construct accurate user representations based on the rich amounts of data generated through interactions with these systems. This paper presents a comprehensive survey of the current state, evolution, and future directions of user modeling and profiling research. We provide a historical overview, tracing the development from early stereotype models to the latest deep learning techniques, and propose a novel taxonomy that encompasses all active topics in this research area, including recent trends. Our survey highlights the paradigm shifts towards more sophisticated user profiling methods, emphasizing implicit data collection, multi-behavior modeling, and the integration of graph data structures. We also address the critical need for privacy-preserving techniques and the push towards explainability and fairness in user modeling approaches. By examining the definitions of core terminology, we aim to clarify ambiguities and foster a clearer understanding of the field by proposing two novel encyclopedic definitions of the main terms. Furthermore, we explore the application of user modeling in various domains, such as fake news detection, cybersecurity, and personalized education. This survey serves as a comprehensive resource for researchers and practitioners, offering insights into the evolution of user modeling and profiling and guiding the development of more personalized, ethical, and effective AI systems.

Recently text-to-image models have gained widespread attention in the community due to their controllable and high-quality generation ability. However, the robustness of such models and their potential ethical issues have not been fully explored. In this paper, we introduce Universal Semantic Trigger, a meaningless token sequence that can be added at any location within the input text yet can induce generated images towards a preset semantic target.To thoroughly investigate it, we propose Semantic Gradient-based Search (SGS) framework. SGS automatically discovers the potential universal semantic triggers based on the given semantic targets. Furthermore, we design evaluation metrics to comprehensively evaluate semantic shift of images caused by these triggers. And our empirical analyses reveal that the mainstream open-source text-to-image models are vulnerable to our triggers, which could pose significant ethical threats. Our work contributes to a further understanding of text-to-image synthesis and helps users to automatically auditing their models before deployment.

Symbolic Music Alignment is the process of matching performed MIDI notes to corresponding score notes. In this paper, we introduce a reinforcement learning (RL)-based online symbolic music alignment technique. The RL agent - an attention-based neural network - iteratively estimates the current score position from local score and performance contexts. For this symbolic alignment task, environment states can be sampled exhaustively and the reward is dense, rendering a formulation as a simplified offline RL problem straightforward. We evaluate the trained agent in three ways. First, in its capacity to identify correct score positions for sampled test contexts; second, as the core technique of a complete algorithm for symbolic online note-wise alignment; and finally, as a real-time symbolic score follower. We further investigate the pitch-based score and performance representations used as the agent's inputs. To this end, we develop a second model, a two-step Dynamic Time Warping (DTW)-based offline alignment algorithm leveraging the same input representation. The proposed model outperforms a state-of-the-art reference model of offline symbolic music alignment.

Harriet Tubman didn't give many interviews in her lifetime, and when she did, they were generally conducted by one of her friends, Sarah Hopkins Bradford, a White children's book author in Upstate New York, where Tubman spent the last decades of her life. The result of those interviews were two biographies, published in 1869 and 1886. Though Bradford obviously admired Tubman, the books suffer from her sometimes patronizing attitude toward her subject, her use of racial slurs and her awkward attempts to re-create the speech patterns of a Black woman raised enslaved in Maryland. Some of the long "quotes" from Tubman were completely made up, and it shows. So I was curious to see what would happen recently when I had my own "interview" with Tubman -- using the online educator Khan Academy's new artificial intelligence learning tool Khanmigo, which enables users to have live chats with dozens of simulated historical figures like Abigail Adams, Genghis Khan, Montezuma and Winston Churchill. And if so, would it come off horribly, a 21st-century minstrelsy?

Artificial intelligence continues to seep into every aspect of our lives: search results, chatbots, images on social media, viral videos, documentaries about dead celebrities. A new class of emerging AI-powered services can take audio clips from voice recordings and build models off them. Anything you type into a computer can be spit out as an impression of that person's voice. Proponents of AI voice cloning see these tools as a way to make life a little easier for content creators. The robovoices can be used to fix mistakes, read ads, or perform other mundane duties.

In a new podcast series, Everyday AI, host Jon Whittle (CSIRO) explores the AI that is already shaping our lives. With the help of expert guests, he explores how AI is used in creative industries, health, conservation, sports and space. Episode 4: AI and citizen science – AI in ecology This episode features Jessie Barry from Cornell University's Macaulay Library and Merlin Bird ID, ichthyologist Mark McGrouther, and Google's Megha Malpani. Episode 6: The final frontier – AI in space This episode features Astrophysicist Kirsten Banks, NASA researcher Dr Raymond Francis, and Research Astronomer Dr Ivy Wong.

With the increasing digitisation of society and human communication, text data are becoming more important for research in the social and behavioural sciences (Gentzkow, Kelly, and Taddy 2019; Salganik 2019). Advances made in natural language processing (NLP) in particular have led to exciting insights derived from text data (e.g., on emotional responses to the pandemic (Kleinberg, Vegt, and Mozes 2020) or on the rhetoric around immigration in political speeches (Card et al. 2022); for an overview, see (Boyd and Schwartz 2021)). Importantly, the use of computational techniques to quantify and analyse text data has triggered a demand, especially for large datasets (often of several tens of thousands of documents) that can be harnessed for machine learning approaches (e.g., (Socher et al. 2013; Lewis et al. 2020)). That status quo of a need for larger datasets and an appetite to use text data for the study of social science phenomena has resulted in a dilemma: many of the important questions require targeted, primary data collection or access to potentially sensitive data. However, such data are hard to obtain, not because they do not exist but because sharing them is constrained by data protection regulations and ethical concerns. One potential consequence is that research activity may be biased toward topics for which suitable data is more readily available rather than those most important. One of the few viable solutions to this dilemma is automated text anonymisation; that is, the large-scale processing of text data so that individuals cannot be identified from the resulting output. Such a method would allow for the flow of sensitive data so that the staggering potential of text data can be exploited for scientific progress. With this paper and the tool it introduces, we seek to enable researchers to work with such sensitive data in a way that protects the privacy of individuals whilst retaining the usefulness of anonymised data for computational text analysis.