Large Language Models (LLMs) are transforming geospatial artificial intelligence (GeoAI), offering new capabilities in data processing, spatial analysis, and decision support. This paper examines the open-source paradigm's critical role in this transformation. While proprietary LLMs offer accessibility, they often limit the customization, interoperability, and transparency vital for specialized geospatial tasks. Conversely, open-source alternatives significantly advance Geographic Information Science (GIScience) by fostering greater adaptability, reproducibility, and community-driven innovation. Open frameworks empower researchers to tailor solutions, integrate cutting-edge methodologies (e.g., reinforcement learning, advanced spatial indexing), and align with FAIR (Findable, Accessible, Interoperable, and Reusable) principles. However, the growing reliance on any LLM necessitates careful consideration of security vulnerabilities, ethical risks, and robust governance for AI-generated geospatial outputs. This paper argues that GIScience advances best not through a single model type, but by cultivating a diverse, interoperable ecosystem combining open-source foundations for innovation, custom geospatial models, and interdisciplinary collaboration. By critically evaluating the opportunities and challenges of open-source LLMs within the broader GeoAI landscape, this work contributes to a thorough discourse on leveraging LLMs to effectively advance spatial research, policy, and decision-making in an equitable, sustainable, and scientifically rigorous manner.

The proliferation of AI-driven systems presents a fundamental challenge to Human-Computer Interaction (HCI) and Computer-Supported Cooperative Work (CSCW), often diminishing user agency and failing to account for value pluralism. Current approaches to value alignment, which rely on centralized, top-down definitions, lack the mechanisms for meaningful contestability. This leaves users and communities unable to challenge or shape the values embedded in the systems that govern their digital lives, creating a crisis of legitimacy and trust. This paper introduces Community-Defined AI Value Pluralism (CDAVP), a socio-technical framework that addresses this gap. It reframes the design problem from achieving a single aligned state to infrastructuring a dynamic ecosystem for value deliberation and application. At its core, CDAVP enables diverse, self-organizing communities to define and maintain explicit value profiles - rich, machine-readable representations that can encompass not only preferences but also community-specific rights and duties. These profiles are then contextually activated by the end-user, who retains ultimate control (agency) over which values guide the AI's behavior. AI applications, in turn, are designed to transparently interpret these profiles and moderate conflicts, adhering to a set of non-negotiable, democratically-legitimated meta-rules. The designer's role shifts from crafting static interfaces to becoming an architect of participatory ecosystems. We argue that infrastructuring for pluralism is a necessary pathway toward achieving robust algorithmic accountability and genuinely contestable, human-centric AI.

Major advertising platforms recently increased privacy protections by limiting advertisers' access to individual-level data. Instead of providing access to granular raw data, the platforms only allow a limited number of aggregate queries to a dataset, which is further protected by adding differentially private noise. This paper studies whether and how advertisers can design effective targeting policies within these restrictive privacy preserving data environments. To achieve this, I develop a probabilistic machine learning method based on Bayesian optimization, which facilitates dynamic data exploration. Since Bayesian optimization was designed to sample points from a function to find its maximum, it is not applicable to aggregate queries and to targeting. Therefore, I introduce two innovations: (i) integral updating of posteriors which allows to select the best regions of the data to query rather than individual points and (ii) a targeting-aware acquisition function that dynamically selects the most informative regions for the targeting task. I identify the conditions of the dataset and privacy environment that necessitate the use of such a "smart" querying strategy. I apply the strategic querying method to the Criteo AI Labs dataset for uplift modeling (Diemert et al., 2018) that contains visit and conversion data from 14M users. I show that an intuitive benchmark strategy only achieves 33% of the non-privacy-preserving targeting potential in some cases, while my strategic querying method achieves 97-101% of that potential, and is statistically indistinguishable from Causal Forest (Athey et al., 2019): a state-of-the-art non-privacy-preserving machine learning targeting method.

The severity of natural disasters is increasing every year, impacting many people's lives. During the response phase of disasters, airports are important hubs where relief aid arrives and people need to be evacuated. However, the airport often forms a bottleneck in these relief operations due to the sudden need for increased capacity. Limited research has been done on the operational side of airport disaster management. Experts identify the main problems as, first, the asymmetry of information between the airport and incoming flights, and second, the lack of resources. The goal of this research is to understand the effects of incomplete knowledge of incoming flights with different resource allocation strategies on the performance of cargo handling operations at an airport after a natural disaster. An agent-based model is created, implementing realistic offloading strategies with different degrees of information uncertainty. Model calibration and verification are performed with experts in the field. The model performance is measured by the average turnaround time, which is divided into offloading time, boarding time, and cumulative waiting times. The results show that the effects of one unplanned aircraft are negligible. However, all waiting times increase with more arriving unplanned aircraft.

Kronecker-factored approximate curvature (KFAC) is arguably one of the most prominent curvature approximations in deep learning. Its applications range from optimization to Bayesian deep learning, training data attribution with influence functions, and model compression or merging. While the intuition behind KFAC is easy to understand, its implementation is tedious: It comes in many flavours, has common pitfalls when translating the math to code, and is challenging to test, which complicates ensuring a properly functioning implementation. Some of the authors themselves have dealt with these challenges and experienced the discomfort of not being able to fully test their code. Thanks to recent advances in understanding KFAC, we are now able to provide test cases and a recipe for a reliable KFAC implementation. This tutorial is meant as a ground-up introduction to KFAC. In contrast to the existing work, our focus lies on providing both math and code side-by-side and providing test cases based on the latest insights into KFAC that are scattered throughout the literature. We hope this tutorial provides a contemporary view of KFAC that allows beginners to gain a deeper understanding of this curvature approximation while lowering the barrier to its implementation, extension, and usage in practice.

Privacy protection laws, such as the GDPR, grant individuals the right to request the forgetting of their personal data not only from databases but also from machine learning (ML) models trained on them. Machine unlearning has emerged as a practical means to facilitate model forgetting of data instances seen during training. Although some existing machine unlearning methods guarantee exact forgetting, they are typically costly in computational terms. On the other hand, more affordable methods do not offer forgetting guarantees and are applicable only to specific ML models. In this paper, we present \emph{efficient unlearning with privacy guarantees} (EUPG), a novel machine unlearning framework that offers formal privacy guarantees to individuals whose data are being unlearned. EUPG involves pre-training ML models on data protected using privacy models, and it enables {\em efficient unlearning with the privacy guarantees offered by the privacy models in use}. Through empirical evaluation on four heterogeneous data sets protected with $k$-anonymity and $ε$-differential privacy as privacy models, our approach demonstrates utility and forgetting effectiveness comparable to those of exact unlearning methods, while significantly reducing computational and storage costs. Our code is available at https://github.com/najeebjebreel/EUPG.

Anomaly detection plays a critical role in Autonomous Vehicles (AVs) by identifying unusual behaviors through perception systems that could compromise safety and lead to hazardous situations. Current approaches, which often rely on predefined thresholds or supervised learning paradigms, exhibit reduced efficacy when confronted with unseen scenarios, sensor noise, and occlusions, leading to potential safety-critical failures. Moreover, supervised methods require large annotated datasets, limiting their real-world feasibility. To address these gaps, we propose an anomaly detection framework based on Inverse Reinforcement Learning (IRL) to infer latent driving intentions from sequential perception data, thus enabling robust identification. Specifically, we present Trajectory-Reward Guided Adaptive Pre-training (TRAP), a novel IRL framework for anomaly detection, to address two critical limitations of existing methods: noise robustness and generalization to unseen scenarios. Our core innovation is implicitly learning temporal credit assignments via reward and worst-case supervision. We leverage pre-training with variable-horizon sampling to maximize time-to-consequence, resulting in early detection of behavior deviation. Experiments on 14,000+ simulated trajectories demonstrate state-of-the-art performance, achieving 0.90 AUC and 82.2\% F1-score - outperforming similarly trained supervised and unsupervised baselines by 39\% on Recall and 12\% on F1-score, respectively. Similar performance is achieved while exhibiting robustness to various noise types and generalization to unseen anomaly types. Our code will be available at: https://github.com/abastola0/TRAP.git

The onset of spontaneous thoughts are reflective of dynamic interactions between cognition, emotion, and attention. Typically, these experiences are studied through subjective appraisals that focus on their triggers, phenomenology, and emotional salience. In this work, we use linguistic signatures to investigate D ej ` a Vu, Involuntary Autobiographical Memories, and Unexpected Thoughts. Specifically, we analyze the inherent characteristics of the linguistic patterns in participant generated descriptions of these thought types. We show how, by positioning language as a window into spontaneous cognition, existing theories on these attentional states can be updated and reaffirmed. Our findings align with prior research, reinforcing that D ej ` a Vu is a metacognitive experience characterized by abstract and spatial language, Involuntary Autobiographical Memories are rich in personal and emotionally significant detail, and Unexpected Thoughts are marked by unpredictability and cognitive disruption. This work is demonstrative of languages' potential to reveal deeper insights into how internal spontaneous cognitive states manifest through expression.

Machine learning methods are increasingly applied to analyze health-related public discourse based on large-scale data, but questions remain regarding their ability to accurately detect different types of health sentiments. Especially, Large Language Models (LLMs) have gained attention as a powerful technology, yet their accuracy and feasibility in capturing different opinions and perspectives on health issues are largely unexplored. Thus, this research examines how accurate the three prominent LLMs (GPT, Gemini, and LLAMA) are in detecting risk-promoting versus health-supporting sentiments across two critical public health topics: Human Papillomavirus (HPV) vaccination and heated tobacco products (HTPs). Drawing on data from Facebook and Twitter, we curated multiple sets of messages supporting or opposing recommended health behaviors, supplemented with human annotations as the gold standard for sentiment classification. The findings indicate that all three LLMs generally demonstrate substantial accuracy in classifying risk-promoting and health-supporting sentiments, although notable discrepancies emerge by platform, health issue, and model type. Specifically, models often show higher accuracy for risk-promoting sentiment on Facebook, whereas health-supporting messages on Twitter are more accurately detected. An additional analysis also shows the challenges LLMs face in reliably detecting neutral messages. These results highlight the importance of carefully selecting and validating language models for public health analyses, particularly given potential biases in training data that may lead LLMs to overestimate or underestimate the prevalence of certain perspectives.

As AI hype continues to grow, organizations face pressure to broadcast or downplay purported AI initiatives - even when contrary to truth. This paper introduces AI-washing as overstating (deceptive boasting) or understating (deceptive denial) a company's real AI usage. A 2x2 experiment (N = 401) examines how these false claims affect consumer attitudes and purchase intentions. Results reveal a pronounced asymmetry: deceptive denial evokes more negative moral judgments than honest negation, while deceptive boasting has no effects. We show that perceived betrayal mediates these outcomes. By clarifying how AI-washing erodes trust, the study highlights clear ethical implications for policymakers, marketers, and researchers striving for transparency.