On-road air pollution exhibits substantial variability over short distances due to emission sources, dilution, and physicochemical processes. Integrating mobile monitoring data with street view images (SVIs) holds promise for predicting local air pollution. However, algorithms, sampling strategies, and image quality introduce extra errors due to a lack of reliable references that quantify their effects. To bridge this gap, we employed 314 taxis to monitor NO, NO2, PM2.5 and PM10 dynamically and sampled corresponding SVIs, aiming to develop a reliable strategy. We extracted SVI features from ~ 382,000 streetscape images, which were collected at various angles (0{\deg}, 90{\deg}, 180{\deg}, 270{\deg}) and ranges (buffers with radii of 100m, 200m, 300m, 400m, 500m). Also, three machine learning algorithms alongside the linear land-used regression (LUR) model were experimented with to explore the influences of different algorithms. Four typical image quality issues were identified and discussed. Generally, machine learning methods outperform linear LUR for estimating the four pollutants, with the ranking: random forest > XGBoost > neural network > LUR. Compared to single-angle sampling, the averaging strategy is an effective method to avoid bias of insufficient feature capture. Therefore, the optimal sampling strategy is to obtain SVIs at a 100m radius buffer and extract features using the averaging strategy. This approach achieved estimation results for each aggregation location with absolute errors almost less than 2.5 {\mu}g/m^2 or ppb. Overexposure, blur, and underexposure led to image misjudgments and incorrect identifications, causing an overestimation of road features and underestimation of human-activity features, contributing to inaccurate NO, NO2, PM2.5 and PM10 estimation.

Over the last few decades, machine learning (ML) applications have grown exponentially, yielding several benefits to society. However, these benefits are tempered with concerns of discriminatory behaviours exhibited by ML models. In this regard, fairness in machine learning has emerged as a priority research area. Consequently, several fairness metrics and algorithms have been developed to mitigate against discriminatory behaviours that ML models may possess. Yet still, very little attention has been paid to the problem of naturally occurring changes in data patterns (\textit{aka} data distributional drift), and its impact on fairness algorithms and metrics. In this work, we study this problem comprehensively by analyzing 4 fairness-unaware baseline algorithms and 7 fairness-aware algorithms, carefully curated to cover the breadth of its typology, across 5 datasets including public and proprietary data, and evaluated them using 3 predictive performance and 10 fairness metrics. In doing so, we show that (1) data distributional drift is not a trivial occurrence, and in several cases can lead to serious deterioration of fairness in so-called fair models; (2) contrary to some existing literature, the size and direction of data distributional drift is not correlated to the resulting size and direction of unfairness; and (3) choice of, and training of fairness algorithms is impacted by the effect of data distributional drift which is largely ignored in the literature. Emanating from our findings, we synthesize several policy implications of data distributional drift on fairness algorithms that can be very relevant to stakeholders and practitioners.

Societal risk emanating from how recommender algorithms disseminate content online is now well documented. Emergent regulation aims to mitigate this risk through ethical audits and enabling new research on the social impact of algorithms. However, there is currently a need for tools and methods that enable such evaluation. This paper presents ARTAI, an evaluation environment that enables large-scale assessments of recommender algorithms to identify harmful patterns in how content is distributed online and enables the implementation of new regulatory requirements for increased transparency in recommender systems.

In our previous research, we provided a reasoning system (called LeSAC) based on argumentation theory to provide legal support to designers during the design process. Building on this, this paper explores how to provide designers with effective explanations for their legally relevant design decisions. We extend the previous system for providing explanations by specifying norms and the key legal or ethical principles for justifying actions in normative contexts. Considering that first-order logic has strong expressive power, in the current paper we adopt a first-order deontic logic system with deontic operators and preferences. We illustrate the advantages and necessity of introducing deontic logic and designing explanations under LeSAC by modelling two cases in the context of autonomous driving. In particular, this paper also discusses the requirements of the updated LeSAC to guarantee rationality, and proves that a well-defined LeSAC can satisfy the rationality postulate for rule-based argumentation frameworks. This ensures the system's ability to provide coherent, legally valid explanations for complex design decisions.

Identification of suspects based on partial and smudged fingerprints, commonly referred to as fingermarks or latent fingerprints, presents a significant challenge in the field of fingerprint recognition. Although fixed-length embeddings have shown effectiveness in recognising rolled and slap fingerprints, the methods for matching latent fingerprints have primarily centred around local minutiae-based embeddings, failing to fully exploit global representations for matching purposes. Consequently, enhancing latent fingerprints becomes critical to ensuring robust identification for forensic investigations. Current approaches often prioritise restoring ridge patterns, overlooking the fine-macroeconomic details crucial for accurate fingerprint recognition. To address this, we propose a novel approach that uses generative adversary networks (GANs) to redefine Latent Fingerprint Enhancement (LFE) through a structured approach to fingerprint generation. By directly optimising the minutiae information during the generation process, the model produces enhanced latent fingerprints that exhibit exceptional fidelity to ground-truth instances. This leads to a significant improvement in identification performance. Our framework integrates minutiae locations and orientation fields, ensuring the preservation of both local and structural fingerprint features. Extensive evaluations conducted on two publicly available datasets demonstrate our method's dominance over existing state-of-the-art techniques, highlighting its potential to significantly enhance latent fingerprint recognition accuracy in forensic applications.

Stance detection (SD) identifies a text's position towards a target, typically labeled as favor, against, or none. We introduce Open-Target Stance Detection (OTSD), the most realistic task where targets are neither seen during training nor provided as input. We evaluate Large Language Models (LLMs) GPT-4o, GPT-3.5, Llama-3, and Mistral, comparing their performance to the only existing work, Target-Stance Extraction (TSE), which benefits from predefined targets. Unlike TSE, OTSD removes the dependency of a predefined list, making target generation and evaluation more challenging. We also provide a metric for evaluating target quality that correlates well with human judgment. Our experiments reveal that LLMs outperform TSE in target generation when the real target is explicitly and not explicitly mentioned in the text. Likewise, for stance detection, LLMs excel in explicit cases with comparable performance in non-explicit in general.

A fifth of GPs are using artificial intelligence (AI) tools such as ChatGPT to help with tasks such as writing letters for their patients after appointments, according to a survey. The survey, published in the journal BMJ Health and Care Informatics, spoke to 1,006 GPs. They were asked whether they had ever used any form of AI chatbot in their clinical practice, such as ChatGPT, Bing AI or Google's Gemini, and were then asked what they used these tools for. One in five of the respondents said that they had used generative AI tools in their clinical practice and, of these, almost a third (29%) said that they had used them to generate documentation after patient appointments, while 28% said that they had used the tools to suggest a different diagnosis. A quarter of respondents said they had used the AI tools to suggest treatment options for their patients.

Gov. Gavin Newsom on Tuesday signed into law two bills that will give actors more protections over their digital likenesses, addressing concerns brought up during last year's Hollywood strike led by performers guild SAG-AFTRA. One of the bills, AB1836, prohibits and penalizes the making and distribution of a deceased person's digital replica without permission from their estate. The other legislation, AB2602, makes a contract entered after Jan. 1, 2025, unenforceable if a digital replica of an actor was used when the individual could have performed the work in person, if the contract did not include a reasonably specific description of how the digital replica would be used and if the actor was not represented by their lawyer or labor union when the deal was signed. "No one should live in fear of becoming someone else's unpaid digital puppet," said Duncan Crabtree-Ireland, SAG-AFTRA's national executive director and chief negotiator in a statement. Newsom has led the way in protecting people -- and families -- from A.I. replication without real consent."

California has given the go-ahead to a landmark AI bill to protect performers' digital likenesses. On Tuesday, Governor Gavin Newsom signed Assembly Bill 2602, which will go into effect on January 1, 2025. The bill requires studios and other employers to get consent before using "digital replicas" of performers. Newsom also signed AB 1836, which grants similar rights to deceased performers, requiring their estate's permission before using their AI likenesses. AB 2602, introduced in April, covers film, TV, video games, commercials, audiobooks and non-union performing jobs.

In July, Elon Musk made a bold prediction: that his artificial intelligence startup xAI would release "the most powerful AI in the world," a model called Grok 3, by this December. The bulk of that AI's training, Musk said, would happen at a "massive new training center" in Memphis, which he bragged had been built in 19 days. But many residents of Memphis were taken by surprise, including city council members who said they were given no input about the project or its potential impacts on the city. And in the months since, an outcry has grown among community members and environmental groups, who warn of the plant's potential negative impact on air quality, water access, and grid stability, especially for nearby neighborhoods that have suffered from industrial pollution for decades. These activists also contend that the company is illegally operating gas turbines.