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.

Though there is much interest in fair AI systems, the problem of fairness noncompliance -- which concerns whether fair models are used in practice -- has received lesser attention. Zero-Knowledge Proofs of Fairness (ZKPoF) address fairness noncompliance by allowing a service provider to verify to external parties that their model serves diverse demographics equitably, with guaranteed confidentiality over proprietary model parameters and data. They have great potential for building public trust and effective AI regulation, but no previous techniques for ZKPoF are fit for real-world deployment. We present OATH, the first ZKPoF framework that is (i) deployably efficient with client-facing communication comparable to in-the-clear ML as a Service query answering, and an offline audit phase that verifies an asymptotically constant quantity of answered queries, (ii) deployably flexible with modularity for any score-based classifier given a zero-knowledge proof of correct inference, (iii) deployably secure with an end-to-end security model that guarantees confidentiality and fairness across training, inference, and audits. We show that OATH obtains strong robustness against malicious adversaries at concretely efficient parameter settings. Notably, OATH provides a 1343x improvement to runtime over previous work for neural network ZKPoF, and scales up to much larger models -- even DNNs with tens of millions of parameters.

Annotation The article proposes a method for forming a dedicated operational context in course of development and implementation of autonomous corporate management systems based on example of autonomous systems for a board of directors. The significant part of the operational context for autonomous company management systems is the regulatory and legal environment within which corporations operate. In order to create a special operational context for autonomous artificial intelligence systems, the wording of local regulatory documents can be simultaneously presented in two versions: for use by people and for use by autonomous systems. In this case, the artificial intelligence system will get a well-defined operational context that allows such a system to perform functions within the required standards. Local regulations that provide for the specifics of the joint work of individuals and autonomous artificial intelligence systems can create the basis of the relevant legislation governing the development and implementation of autonomous systems.