Apple has moved to settle a five-year-old class action lawsuit over Siri privacy. Reuters reports that the proposed settlement was filed on Tuesday in Oakland, CA. The company agreed to pay 95 million to class members, estimated to be tens of millions of Siri-enabled device owners. US District Judge Jeffrey White needs to approve the settlement before it becomes official. The lawsuit stemmed from a 2019 report that Apple quality control contractors could regularly hear sensitive info accidentally recorded by the voice assistant's "Hey Siri" feature. The clips were said to include medical information, criminal activities and even "sexual encounters."

So much has happened in the AI space over the course of the past 12 months. We've reported on some of the larger, and lesser-covered, stories in our regular monthly digests. We look back through the archives and pick out one story from each of our digests. Interview with Bo Li: A comprehensive assessment of trustworthiness in GPT models Bo Li and colleagues won an outstanding datasets and benchmark track award at NeurIPS 2023 for their work DecodingTrust: A Comprehensive Assessment of Trustworthiness in GPT Models. In this interview, Bo told us about the research, the team's methodology, and key findings. AAAI 2024 takes place This month saw the running of the 38th Annual AAAI Conference.

Large language models trained on web-scale corpora can memorize undesirable datapoints such as incorrect facts, copyrighted content or sensitive data. Recently, many machine unlearning algorithms have been proposed that aim to `erase' these datapoints from trained models -- that is, revert model behavior to be similar to a model that had never been trained on these datapoints. However, evaluating the success of unlearning algorithms remains an open challenge. In this work, we propose the RESTOR framework for machine unlearning, which evaluates the ability of unlearning algorithms to perform targeted data erasure from models, by evaluating the ability of models to forget the knowledge introduced in these data points, while simultaneously recovering the model's knowledge state had it not encountered these datapoints. RESTOR helps uncover several novel insights about popular unlearning algorithms, and the mechanisms through which they operate -- for instance, identifying that some algorithms merely emphasize forgetting, and that localizing unlearning targets can enhance unlearning performance.

Media bias in news articles arises from the political polarisation of media outlets, which can reinforce societal stereotypes and beliefs. Reporting on the same event often varies significantly between outlets, reflecting their political leanings through polarised language and focus. Although previous studies have attempted to generate bias-free summaries from multiperspective news articles, they have not effectively addressed the challenge of mitigating inherent media bias. To address this gap, we propose \textbf{NeutraSum}, a novel framework that integrates two neutrality losses to adjust the semantic space of generated summaries, thus minimising media bias. These losses, designed to balance the semantic distances across polarised inputs and ensure alignment with expert-written summaries, guide the generation of neutral and factually rich summaries. To evaluate media bias, we employ the political compass test, which maps political leanings based on economic and social dimensions. Experimental results on the Allsides dataset demonstrate that NeutraSum not only improves summarisation performance but also achieves significant reductions in media bias, offering a promising approach for neutral news summarisation.

California lawmakers passed roughly 1,200 bills last year, including some that resulted in unforeseeable wins by Republicans, promising protections for consumers and small strides for those in the entertainment industry. In the end, Gov. Gavin Newsom signed about 84% of the bills he received. Many of those laws take effect today, Jan. 1, as California rings in a new year. Cannabis cafes are legal: You can now hang out at dispensaries like you would a restaurant or cafe, thanks to AB 1775. The new law brings an Amsterdam-style approach to marijuana use, by allowing cannabis retailers to make and sell food and nonalcoholic beverages at what will be known as cannabis cafes or lounges.

The increasing use of AI technologies has led to increasing AI incidents, posing risks and causing harm to individuals, organizations, and society. This study recognizes and addresses the lack of standardized protocols for reliably and comprehensively gathering such incident data crucial for preventing future incidents and developing mitigating strategies. Specifically, this study analyses existing open-access AI-incident databases through a systematic methodology and identifies nine gaps in current AI incident reporting practices. Further, it proposes nine actionable recommendations to enhance standardization efforts to address these gaps. Ensuring the trustworthiness of enabling technologies such as AI is necessary for sustainable digital transformation. Our research promotes the development of standards to prevent future AI incidents and promote trustworthy AI, thus facilitating achieving the UN sustainable development goals. Through international cooperation, stakeholders can unlock the transformative potential of AI, enabling a sustainable and inclusive future for all.

Artificial intelligence (AI) is transforming numerous aspects of contemporary life, with advancements fueled largely by the training of models on extensive datasets (Pouyanfar et al. 2018; S. Dong et al. 2021; Bialkova 2024). This is particularly evident in areas like autonomous driving (S. Liu et al. 2024; C. Cui et al. 2024), generative AI (Feuerriegel et al. 2024; Huang et al. 2024), and medical image processing (Tian et al. 2024; Alzubaidi et al. 2024), which depend on large-scale model training. As these models expand to encompass hundreds of billions of parameters, the need for high-quality training data becomes critical (Zhao et al. 2023; Minaee et al. 2024). Training such large-scale models often requires tens to hundreds of trillions of tokens, substantial interdisciplinary effort over months, and a vast array of computational resources, including thousands of GPUs and high levels of energy consumption (Achiam et al. 2023; Touvron, Lavril, et al. 2023; Touvron, Martin, et al. 2023; Chowdhery et al. 2023). A core challenge is ensuring that training data is meticulously curated--ineffective data selection can yield models that underperform, fall short of desired objectives, and waste considerable resources (Chowdhery et al. 2023; Gunasekar et al. 2023b). Thus, once model architecture and algorithms are defined, the quality of the training data becomes paramount to a model's success, significantly influencing the performance and relevance of AI technologies across various domains (Hamid 2023; Zha et al. 2023).By focusing on data quality, small-scale models can achieve performance comparable to much larger models. For instance, Phi-1.5 achieves performance on par with models 5 times its size, while Phi-2 matches or even surpasses the performance of models 25 times larger(Gunasekar et al. 2023a; Y. Li et al. 2023).

Data is essential for secondary use, but ensuring its privacy while allowing such use is a critical challenge. Various techniques have been proposed to address privacy concerns in data sharing and publishing. However, these methods often degrade data utility, impacting the performance of machine learning (ML) models. Our research identifies key limitations in existing optimization models for privacy preservation, particularly in handling categorical variables, assessing data utility, and evaluating effectiveness across diverse datasets. We propose a novel multi-objective optimization model that simultaneously minimizes information loss and maximizes protection against attacks. This model is empirically validated using diverse datasets and compared with two existing algorithms. We assess information loss, the number of individuals subject to linkage or homogeneity attacks, and ML performance after anonymization. The results indicate that our model achieves lower information loss and more effectively mitigates the risk of attacks, reducing the number of individuals susceptible to these attacks compared to alternative algorithms in some cases. Additionally, our model maintains comparative ML performance relative to the original data or data anonymized by other methods. Our findings highlight significant improvements in privacy protection and ML model performance, offering a comprehensive framework for balancing privacy and utility in data sharing.

With the widespread use of social media, user-generated content has surged on online platforms. When such content includes hateful, abusive, offensive, or cyberbullying behavior, it is classified as toxic speech, posing a significant threat to the online ecosystem's integrity and safety. While manual content moderation is still prevalent, the overwhelming volume of content and the psychological strain on human moderators underscore the need for automated toxic speech detection. Previously proposed detection methods often rely on large annotated datasets; however, acquiring such datasets is both costly and challenging in practice. To address this issue, we propose an uncertainty-guided firewall for toxic speech in few-shot scenarios, U-GIFT, that utilizes self-training to enhance detection performance even when labeled data is limited. Specifically, U-GIFT combines active learning with Bayesian Neural Networks (BNNs) to automatically identify high-quality samples from unlabeled data, prioritizing the selection of pseudo-labels with higher confidence for training based on uncertainty estimates derived from model predictions. Extensive experiments demonstrate that U-GIFT significantly outperforms competitive baselines in few-shot detection scenarios. In the 5-shot setting, it achieves a 14.92\% performance improvement over the basic model. Importantly, U-GIFT is user-friendly and adaptable to various pre-trained language models (PLMs). It also exhibits robust performance in scenarios with sample imbalance and cross-domain settings, while showcasing strong generalization across various language applications. We believe that U-GIFT provides an efficient solution for few-shot toxic speech detection, offering substantial support for automated content moderation in cyberspace, thereby acting as a firewall to promote advancements in cybersecurity.

The evolution of agentic systems represents a significant milestone in artificial intelligence and modern software systems, driven by the demand for vertical intelligence tailored to diverse industries. These systems enhance business outcomes through adaptability, learning, and interaction with dynamic environments. At the forefront of this revolution are Large Language Model (LLM) agents, which serve as the cognitive backbone of these intelligent systems. In response to the need for consistency and scalability, this work attempts to define a level of standardization for Vertical AI agent design patterns by identifying core building blocks and proposing a \textbf{Cognitive Skills } Module, which incorporates domain-specific, purpose-built inference capabilities. Building on these foundational concepts, this paper offers a comprehensive introduction to agentic systems, detailing their core components, operational patterns, and implementation strategies. It further explores practical use cases and examples across various industries, highlighting the transformative potential of LLM agents in driving industry-specific applications.