The performance of algorithmic decision rules is largely dependent on the quality of training datasets available to them. Biases in these datasets can raise economic and ethical concerns due to the resulting algorithms' disparate treatment of different groups. In this paper, we propose algorithms for sequentially debiasing the training dataset through adaptive and bounded exploration in a classification problem with costly and censored feedback. Our proposed algorithms balance between the ultimate goal of mitigating the impacts of data biases -- which will in turn lead to more accurate and fairer decisions, and the exploration risks incurred to achieve this goal. Specifically, we propose adaptive bounds to limit the region of exploration, and leverage intermediate actions which provide noisy label information at a lower cost. We analytically show that such exploration can help debias data in certain distributions, investigate how {algorithmic fairness interventions} can work in conjunction with our proposed algorithms, and validate the performance of these algorithms through numerical experiments on synthetic and real-world data.

Given the demand for responsible and trustworthy AI for education, this study evaluates symbolic, sub-symbolic, and neural-symbolic AI (NSAI) in terms of generalizability and interpretability. Our extensive experiments on balanced and imbalanced self-regulated learning datasets of Estonian primary school students predicting 7th-grade mathematics national test performance showed that symbolic and sub-symbolic methods performed well on balanced data but struggled to identify low performers in imbalanced datasets. Interestingly, symbolic and sub-symbolic methods emphasized different factors in their decision-making: symbolic approaches primarily relied on cognitive and motivational factors, while sub-symbolic methods focused more on cognitive aspects, learnt knowledge, and the demographic variable of gender -- yet both largely overlooked metacognitive factors. The NSAI method, on the other hand, showed advantages by: (i) being more generalizable across both classes -- even in imbalanced datasets -- as its symbolic knowledge component compensated for the underrepresented class; and (ii) relying on a more integrated set of factors in its decision-making, including motivation, (meta)cognition, and learnt knowledge, thus offering a comprehensive and theoretically grounded interpretability framework. These contrasting findings highlight the need for a holistic comparison of AI methods before drawing conclusions based solely on predictive performance. They also underscore the potential of hybrid, human-centred NSAI methods to address the limitations of other AI families and move us closer to responsible AI for education. Specifically, by enabling stakeholders to contribute to AI design, NSAI aligns learned patterns with theoretical constructs, incorporates factors like motivation and metacognition, and strengthens the trustworthiness and responsibility of educational data mining.

In this paper, we study the offline sequential feature-based pricing and inventory control problem where the current demand depends on the past demand levels and any demand exceeding the available inventory is lost. Our goal is to leverage the offline dataset, consisting of past prices, ordering quantities, inventory levels, covariates, and censored sales levels, to estimate the optimal pricing and inventory control policy that maximizes long-term profit. While the underlying dynamic without censoring can be modeled by Markov decision process (MDP), the primary obstacle arises from the observed process where demand censoring is present, resulting in missing profit information, the failure of the Markov property, and a non-stationary optimal policy. To overcome these challenges, we first approximate the optimal policy by solving a high-order MDP characterized by the number of consecutive censoring instances, which ultimately boils down to solving a specialized Bellman equation tailored for this problem. Inspired by offline reinforcement learning and survival analysis, we propose two novel data-driven algorithms to solving these Bellman equations and, thus, estimate the optimal policy. Furthermore, we establish finite sample regret bounds to validate the effectiveness of these algorithms. Finally, we conduct numerical experiments to demonstrate the efficacy of our algorithms in estimating the optimal policy. To the best of our knowledge, this is the first data-driven approach to learning optimal pricing and inventory control policies in a sequential decision-making environment characterized by censored and dependent demand. The implementations of the proposed algorithms are available at https://github.com/gundemkorel/Inventory_Pricing_Control

This photo posted by DOGE on Feb. 11, 2025, shows shelving and cardboard boxes which DODGE says workers at the underground mine facility use to store federal worker retirement papers. The White House announces that it will implement AI technology to improve efficiency in federal records keeping. HISTORIC EFFICIENCY: Fox News Digital has learned that the U.S. Office of Personnel Management (OPM) will post an updated Privacy Impact Assessment (PIA) at the close of business Wednesday that paves the way for artificial intelligence to improve government efficiency and enhance the federal record-keeping process. NOT IN KANSAS ANYMORE: The use of artifical intelligence to reimagine the classic film "The Wizard of Oz" will likely see mixed reactions from fans, experts told Fox News Digital. BAD-FAITH TACTICS: OpenAI escalated its legal battle with Elon Musk by countersuing the Tesla and xAI CEO, claiming in a lawsuit he "has tried every tool available to harm" the company.

This paper proposes a reinforcement learning (RL) framework for insurance reserving that integrates tail-risk sensitivity, macroeconomic regime modeling, and regulatory compliance. The reserving problem is formulated as a finite-horizon Markov Decision Process (MDP), in which reserve adjustments are optimized using Proximal Policy Optimization (PPO) subject to Conditional Value-at-Risk (CVaR) constraints. To enhance policy robustness across varying economic conditions, the agent is trained using a regime-aware curriculum that progressively increases volatility exposure. The reward structure penalizes reserve shortfall, capital inefficiency, and solvency floor violations, with design elements informed by Solvency II and Own Risk and Solvency Assessment (ORSA) frameworks. Empirical evaluations on two industry datasets--Workers' Compensation, and Other Liability--demonstrate that the RL-CVaR agent achieves superior performance relative to classical reserving methods across multiple criteria, including tail-risk control (CVaR$_{0.95}$), capital efficiency, and regulatory violation rate. The framework also accommodates fixed-shock stress testing and regime-stratified analysis, providing a principled and extensible approach to reserving under uncertainty.

A horrific rape and incest video game has sparked fury by encouraging players to be'women's worst nightmare'. The game, titled'No Mercy', centres around a protagonist who rapes his family members including his aunt and his own mother. Players of the vile game are instructed to'never take no for an answer' in their ambition to'subdue' and'own' women. Despite its horrendous themes, the game does not have an official age rating and was available for sale on Steam, the most popular digital game store. The game's developer, Zerat Games, published the game on Steam in March where children as young as 13 can make an account.

Several AI chatbots designed for fantasy and sexual role-playing conversations are leaking user prompts to the web in almost real time, new research seen by WIRED shows. Some of the leaked data shows people creating conversations detailing child sexual abuse, according to the research. Conversations with generative AI chatbots are near instantaneous--you type a prompt and the AI responds. If the systems are configured improperly, however, this can lead to chats being exposed. In March, researchers at the security firm UpGuard discovered around 400 exposed AI systems while scanning the web looking for misconfigurations.

The ChatGPT developer OpenAI has countersued Elon Musk, accusing the billionaire of harassment and asking a US federal judge to stop him from "any further unlawful and unfair action" against the company. OpenAI was co-founded by Musk and its chief executive, Sam Altman, in 2015. However, the two men have been at loggerheads for years over its direction as it transitions from a complex non-profit structure into a more traditional for-profit business. Musk sued OpenAI over its restructuring plans about a year ago, accusing it of betraying its foundational mission by putting the pursuit of profit ahead of the benefit of humanity. He dropped the suit in June, but then filed a fresh one in August.

'The Big Weekend Show' co-hosts discuss Tinder user traffic peaking during'Dating Sunday.' A judge has dismissed kidnapping and conspiracy charges filed against five Massachusetts college students accused of luring a man to their campus in a "Catch a Predator"-style scheme using a dating app. A Worcester District Court judge dismissed the charges against Kelsey Brainard, Isabella Trudeau, Joaquin Smith, Kevin Carroll and Easton Randall on Tuesday. The decision came after lawyers for the teenage Assumption University students claimed prosecutors lacked probable cause and filed motions to dismiss last month. Information regarding the status of a sixth student, charged as a juvenile, was not immediately available.

Generative AI presents a profound challenge to traditional notions of human uniqueness, particularly in creativity. Fueled by neural network based foundation models, these systems demonstrate remarkable content generation capabilities, sparking intense debates about authorship, copyright, and intelligence itself. This paper argues that generative AI represents an alternative form of intelligence and creativity, operating through mathematical pattern synthesis rather than biological understanding or verbatim replication. The fundamental differences between artificial and biological neural networks reveal AI learning as primarily statistical pattern extraction from vast datasets crystallized forms of collective human knowledge scraped from the internet. This perspective complicates copyright theft narratives and highlights practical challenges in attributing AI outputs to individual sources. Rather than pursuing potentially futile legal restrictions, we advocate for human AI synergy. By embracing generative AI as a complementary tool alongside human intuition, context, and ethical judgment, society can unlock unprecedented innovation, democratize creative expression, and address complex challenges. This collaborative approach, grounded in realistic understanding of AIs capabilities and limitations, offers the most promising path forward. Additionally, recognizing these models as products of collective human knowledge raises ethical questions about accessibility ensuring equitable access to these tools could prevent widening societal divides and leverage their full potential for collective benefit.