As AI systems increasingly rely on training data, assessing dataset trustworthiness has become critical, particularly for properties like fairness or bias that emerge at the dataset level. Prior work has used Subjective Logic to assess trustworthiness of individual data, but not to evaluate trustworthiness properties that emerge only at the level of the dataset as a whole. This paper introduces the first formal framework for assessing the trustworthiness of AI training datasets, enabling uncertainty-aware evaluations of global properties such as bias. Built on Subjective Logic, our approach supports trust propositions and quantifies uncertainty in scenarios where evidence is incomplete, distributed, and/or conflicting. We instantiate this framework on the trustworthiness property of bias, and we experimentally evaluate it based on a traffic sign recognition dataset. The results demonstrate that our method captures class imbalance and remains interpretable and robust in both centralized and federated contexts.

Artificial Intelligence (AI) systems, particularly neural networks, are increasingly employed in critical applications such as healthcare, finance, and autonomous systems. These systems play an integral role in decision-making, where the trustworthiness of their predictions becomes paramount. Trustworthiness in AI encompasses attributes like reliability, robustness, fairness, and transparency, yet these qualities are often difficult to evaluate, particularly in neural networks, which are typically viewed as "black-box" models. This opacity raises significant concerns about their trustworthiness, especially in sensitive domains where incorrect decisions can lead to severe consequences. Traditional performance metrics like accuracy, precision, and recall measure only the correctness of the model's predictions but fail to capture the confidence and uncertainty associated with those predictions. Confidence calibration, which aligns predicted probabilities with actual outcomes, has emerged as an important tool to address these shortcomings. Well-calibrated models provide predictions where the predicted probability corresponds to the actual likelihood of the event, ensuring that a 70% confidence means the event occurs approximately 70% of the time. However, despite its utility, calibration alone does not fully address the issue of trustworthiness, as it does not account for subjective uncertainty or provide an interpretable way to assess trust across a range of predictions. To address this, we propose the use of subjective logic for trustworthiness quantification in neural networks.

'The CyberGuy' Kurt Knutsson joins'Fox & Friends Weekend' to discuss Elon Musk's lawsuit against OpenAI and its CEO over a contractual breach, saying hes right on this one. "Avengers" and "Her" actress Scarlett Johansson revealed that legal action was likely behind OpenAI removing a voice that sounded eerily like hers. A statement released by NPR on Monday explained that OpenAI CEO Sam Altman reached out to Johansson in September about possibly hiring her to voice the ChatGPT 4.0 system. She claimed he suggested her "comforting" voice "could bridge the gap between tech companies and creatives" and help with the "seismic shift concerning humans and Al." Though she rejected the offer after "much consideration and for personal reasons," Johansson was furious to hear the public discuss how the "Sky" voice system resembled hers. Scarlett Johansson said in a statement that she took legal action against OpenAI CEO Sam Altman and the company.

Scarlett Johansson said Monday that she was "shocked, angered and in disbelief" when she heard that OpenAI used a voice "eerily similar" to hers for its new ChatGPT 4.0 chatbot, even after she had declined to provide her voice. Earlier on Monday, OpenAI announced on X that it would pause the AI voice, known as "Sky," while it addresses "questions about how we chose the voices in ChatGPT." The company said in a blog post that the "Sky" voice was "not an imitation" of Johansson's voice, but that it was recorded by a different professional actor, whose identity the company would not reveal to protect her privacy. But Johansson said in a statement to NPR on Monday that OpenAI's Chief Executive Officer Sam Altman had asked her in September to voice the ChatGPT 4.0 system because he thought her "voice would be comforting to people." She declined, but nine months later, her friends, family and the public noticed how the "Sky" voice resembled hers.

A new top-grossing film that has received global recognition for its cinematic prowess is now being revered by the most successful director of the century. Oscar-winning director Steven Spielberg recently proclaimed Dune: Part Two as a'visual epic' in a new interview, calling it'one of the most brilliant science-fiction films I've ever seen.' Spielberg said his favorite scene in the Blockbuster was watching Timothée Chalamet - who plays Paul Atreides - ride a sandworm. Spielberg has also lavished praise on Denis Villeneuve who directed both Dune films, saying Villeneuve's name will be added to the list of sci-fi filmmakers who have built incredible and unique worlds. 'You have made one of the most brilliant science fiction films I have ever seen,' adding that it'is truly a visual epic and it's also filled with deeply, deeply drawn characters,' Spielberg told Villeneuve in the Director's Cut podcast: Dune: Part Two cleared 82.5 million in its opening weekend, surpassing Oppenheimer which brought in 82.4 million. Since its release, the film has grossed nearly 240 million at the domestic box office and 570 million globally.

After years spent pursuing a career in game development, Italian coder Luca Galante had given up. Uprooting himself from a comfortable life in Rome, he flew to England in the hope of finally making his childhood dream a reality. Yet after countless rejected job applications, Galante found himself flipping Big Macs in Thornton Heath McDonald's. Dejected, he gave up on his digital dream, leaving what he says might be "the worst McDonald's in the UK" to code slot machines for a gambling company. Now, 10 years and one bedroom-made game later, Galante is the proud owner of two Baftas.

Machine Learning (ML) model metrics are designed to monitor performance. But when a model goes into production, many factors influence its performance. The traditional checkpoints may no longer help as organisations look to scale these models (think: scaling from a million to billion credit card users). This is why experts advocate for MLOps, a branch of ML that brings together all the nice things from DevOps and ML. Though a few experts hold MLOps as the best solution available right now, it's still beset by ambiguities.

--Subjective Logic (SL) is one of well-known belief models that can explicitly deal with uncertain opinions and infer unknown opinions based on a rich set of operators of fusing multiple opinions. Due to high simplicity and applicability, SL has been substantially applied in a variety of decision making in the area of cybersecurity, opinion models, trust models, and/or social network analysis. However, SL and its variants have exposed limitations in predicting uncertain opinions in real-world dynamic network data mainly in threefold: (1) a lack of scalability to deal with a large-scale network; (2) limited capability to handle heterogeneous topological and temporal dependencies among node-level opinions; and (3) a high sensitivity with conflicting evidence that may generate counterintuitive opinions derived from the evidence. In this work, we proposed a novel deep learning (DL)- based dynamic opinion inference model while node-level opinions are still formalized based on SL meaning that an opinion has a dimension of uncertainty in addition to belief and disbelief in a binomial opinion (i.e., agree or disagree). The proposed DLbased dynamic opinion inference model overcomes the above three limitations by integrating the following techniques: (1) state-of-the-art DL techniques, such as the Graph Convolutional Network (GCN) and the Gated Recurrent Units (GRU) for modeling the topological and temporal heterogeneous dependency information of a given dynamic network; (2) modeling conflicting opinions based on robust statistics; and (3) a highly scalable inference algorithm to predict dynamic, uncertain opinions in a linear computation time. We validated the outperformance of our proposed DLbased algorithm (i.e., GCN-GRU-opinion model) via extensive comparative performance analysis based on four real-world datasets. In the decision making domain, including the fields of evidence and belief theories, reasoning or managing uncertainty has been studied since 1960s. The examples include Fuzzy Logic, Dempster-Shafer Theory (DST), Transferable Belief Model, and Dezert-Smarandache Theory [6]. These theories deal with uncertainty implicitly. In 1990's, as another variant of DST, Subjective Logic (SL) [16] is proposed to deal with a dimension of uncertainty in subjective opinions more explicitely. SL defines a binomial opinion (e.g., agree vs. disagree) with three dimensions, including belief, disbelief, and uncertainty.

A 29-year-old computer scientist has earned plaudits worldwide for helping develop the algorithm that created the first-ever image of a black hole. Katie Bouman led development of a computer program that made the breakthrough image possible. The remarkable photo, showing a halo of dust and gas 500 million trillion km from Earth, was released on Wednesday. For Dr Bouman, its creation was the realisation of an endeavour previously thought impossible. Excitedly bracing herself for the groundbreaking moment, Dr Bouman was pictured loading the image on her laptop.

In this work we first remember the characteristics of Quantum Rule-Based Systems (QRBS), a concept defined in a previous article by one of the authors of this paper, and we introduce the problem of quantum uncertainty. We assume that the subjective uncertainty that affects the facts of classical RBSs can be treated as a direct consequence of the probabilistic nature of quantum mechanics (QM), and we also assume that the uncertainty associated with a given hypothesis is a consequence of the propagation of the imprecision through the inferential circuits of RBSs. This article does not intend to contribute anything new to the QM field: it is a work of artificial intelligence (AI) that uses QC techniques to solve the problem of uncertainty in RBSs. Bearing the above arguments in mind a quantum model is proposed. This model has been applied to a problem already defined by one of the authors of this work in a previous publication and which is briefly described in this article. Then the model is generalized, and it is thoroughly evaluated. The results obtained show that QC is a valid, effective and efficient method to deal with the inherent uncertainty of RBSs.