The rise of Large Language Models (LLMs) as evaluators offers a scalable alternative to human annotation, yet existing Supervised Fine-Tuning (SFT) for judges approaches often fall short in domains requiring complex reasoning. In this work, we investigate whether LLM judges truly benefit from enhanced reasoning capabilities. Through a detailed analysis of reasoning requirements across evaluation tasks, we reveal a negative correlation between SFT performance gains and the proportion of reasoning-demanding samples - highlighting the limitations of SFT in such scenarios. To address this, we introduce JudgeLRM, a family of judgment-oriented LLMs trained using reinforcement learning (RL) with judge-wise, outcome-driven rewards. JudgeLRM models consistently outperform both SFT-tuned and state-of-the-art reasoning models. Notably, JudgeLRM-3B surpasses GPT-4, and JudgeLRM-7B outperforms DeepSeek-R1 by 2.79% in F1 score, particularly excelling in judge tasks requiring deep reasoning.

I) Introduction Do robots vote? Do machines make decisions instead of us? No, (at least not yet), but this is something that could happen . At the most important level, that of the electoral process, it is noted that it is not determined by the AI, but it is greatly impacted by its multiple applications . New types of online campaigns, driven by AI applications, are replacing traditional ones. The potential for manipulating voters and indirectly influencing the electoral outcome should not be underestimated. Certainly, instances of voter manipulation are not absent from traditional political campaigns, with the only difference being that digital manipulation is often carried out without our knowledge, e.g. by monitoring our behavior on social media. Nevertheless, we should not overlook the positive impact that AI has in the upgrading of democratic institutions by providing a forum for participation in decision - making . In this context, as a first step, we look into the potential jeopardization of democratic processes posed by the use of AI tools. Secondly, we consider the possibility of strengthening democratic processes by using AI, as well as the democratization of AI itself through the possibilities it offers. And thirdly, the impact of AI on the representative system is also discussed. The paper is concluded with recommendations and conclusions. II) Risks posed for democracy Misuse of AI tools can lead to the undermining of democratic political processes or the manipulation of individuals through specific targeting, which will destabilize democracy.

Elon Musk's xAI artificial intelligence firm has acquired Musk's X – the social media platform formerly known as Twitter – for 33bn, marking the latest twist in the billionaire's rapid consolidation of power. The all-stock deal announced on Friday combines two of Musk's multiple portfolio companies, which also include automaker Tesla and SpaceX, and potentially eases Musk's ability to train his AI model known as Grok. Musk announced the transaction in a post on X, saying: "The combination values xAI at 80bn and X at 33bn ( 45B less 12B debt)." "xAI and X's futures are intertwined," he wrote. "Today, we officially take the step to combine the data, models, compute, distribution and talent."

Since the introduction of ChatGPT in 2022, Large language models (LLMs) and Large Multimodal Models (LMM) have transformed content creation, enabling the generation of human-quality content, spanning every medium, text, images, videos, and audio. The chances offered by generative AI models are endless and are drastically reducing the time required to generate content and usually raising the quality of the generation. However, considering the complexity and the difficult traceability of the generated content, the use of these tools provides challenges in attributing AI-generated content. The difficult attribution resides for a variety of reasons, starting from the lack of a systematic fingerprinting of the generated content and ending with the enormous amount of data on which LLMs and LMM are trained, which makes it difficult to connect generated content to the training data. This scenario is raising concerns about intellectual property and ethical responsibilities. To address these concerns, in this paper, we bridge the technological, ethical, and legislative aspects, by proposing a review of the legislative and technological instruments today available and proposing a legal framework to ensure accountability. In the end, we propose three use cases of how these can be combined to guarantee that attribution is respected. However, even though the techniques available today can guarantee a greater attribution to a greater extent, strong limitations still apply, that can be solved uniquely by the development of new attribution techniques, to be applied to LLMs and LMMs.

The rapid growth of artificial intelligence (AI) technologies has changed decision-making in many fields. But, it has also raised major privacy and ethical concerns. However, many AI incidents taxonomies and guidelines for academia, industry, and government lack grounding in real-world incidents. We analyzed 202 real-world AI privacy and ethical incidents. This produced a taxonomy that classifies incident types across AI lifecycle stages. It accounts for contextual factors such as causes, responsible entities, disclosure sources, and impacts. Our findings show insufficient incident reporting from AI developers and users. Many incidents are caused by poor organizational decisions and legal non-compliance. Only a few legal actions and corrective measures exist, while risk-mitigation efforts are limited. Our taxonomy contributes a structured approach in reporting of future AI incidents. Our findings demonstrate that current AI governance frameworks are inadequate. We urgently need child-specific protections and AI policies on social media. They must moderate and reduce the spread of harmful AI-generated content. Our research provides insights for policymakers and practitioners, which lets them design ethical AI. It also support AI incident detection and risk management. Finally, it guides AI policy development. Improved policies will protect people from harmful AI applications and support innovation in AI systems.

Generating high-quality stories spanning thousands of tokens requires competency across a variety of skills, from tracking plot and character arcs to keeping a consistent and engaging style. Due to the difficulty of sourcing labeled datasets and precise quality measurements, most work using large language models (LLMs) for long-form story generation uses combinations of hand-designed prompting techniques to elicit author-like behavior. This is a manual process that is highly dependent on the specific story-generation task. Motivated by the recent success of applying RL with Verifiable Rewards to domains like math and coding, we propose a general story-generation task (Next-Chapter Prediction) and a reward formulation (Verified Rewards via Completion Likelihood Improvement) that allows us to use an unlabeled book dataset as a learning signal for reasoning. We learn to reason over a story's condensed information and generate a detailed plan for the next chapter. Our reasoning is evaluated via the chapters it helps a story-generator create, and compared against non-trained and supervised finetuning (SFT) baselines. Pairwise human judgments reveal the chapters our learned reasoning produces are preferred across almost all metrics, and the effect is more pronounced in Scifi and Fantasy genres.

Fairness studies of algorithmic decision-making systems often simplify complex decision processes, such as bail or loan approvals, into binary classification tasks. However, these approaches overlook that such decisions are not inherently binary (e.g., approve or not approve bail or loan); they also involve non-binary treatment decisions (e.g., bail conditions or loan terms) that can influence the downstream outcomes (e.g., loan repayment or reoffending). In this paper, we argue that non-binary treatment decisions are integral to the decision process and controlled by decision-makers and, therefore, should be central to fairness analyses in algorithmic decision-making. We propose a causal framework that extends fairness analyses and explicitly distinguishes between decision-subjects' covariates and the treatment decisions. This specification allows decision-makers to use our framework to (i) measure treatment disparity and its downstream effects in historical data and, using counterfactual reasoning, (ii) mitigate the impact of past unfair treatment decisions when automating decision-making. We use our framework to empirically analyze four widely used loan approval datasets to reveal potential disparity in non-binary treatment decisions and their discriminatory impact on outcomes, highlighting the need to incorporate treatment decisions in fairness assessments. Moreover, by intervening in treatment decisions, we show that our framework effectively mitigates treatment discrimination from historical data to ensure fair risk score estimation and (non-binary) decision-making processes that benefit all stakeholders.

Since DPO may hinder the LLM's performance in other unrelated tasks, we integrate gradient projection and Fisher information regularization to mitigate the degradation.

The increasing integration of Artificial Intelligence across multiple industry sectors necessitates robust mechanisms for ensuring transparency, trust, and auditability of its development and deployment. This topic is particularly important in light of recent calls in various jurisdictions to introduce regulation and legislation on AI safety. In this paper, we propose a framework for complete verifiable AI pipelines, identifying key components and analyzing existing cryptographic approaches that contribute to verifiability across different stages of the AI lifecycle, from data sourcing to training, inference, and unlearning. This framework could be used to combat misinformation by providing cryptographic proofs alongside AI-generated assets to allow downstream verification of their provenance and correctness. Our findings underscore the importance of ongoing research to develop cryptographic tools that are not only efficient for isolated AI processes, but that are efficiently `linkable' across different processes within the AI pipeline, to support the development of end-to-end verifiable AI technologies.

AI risks are typically framed around physical threats to humanity, a loss of control or an accidental error causing humanity's extinction. However, I argue in line with the gradual disempowerment thesis, that there is an underappreciated risk in the slow and irrevocable decline of human autonomy. As AI starts to outcompete humans in various areas of life, a tipping point will be reached where it no longer makes sense to rely on human decision-making, creativity, social care or even leadership. What may follow is a process of gradual de-skilling, where we lose skills that we currently take for granted. Traditionally, it is argued that AI will gain human skills over time, and that these skills are innate and immutable in humans. By contrast, I argue that humans may lose such skills as critical thinking, decision-making and even social care in an AGI world. The biggest threat to humanity is therefore not that machines will become more like humans, but that humans will become more like machines.