Abstract
The widespread adoption of AI models, especially foundation models (FMs), has made a profound impact on numerous domains. However, it also raises significant ethical concerns, including bias issues. Although numerous efforts have been made to quantify and mitigate social bias in AI models, geographic bias (in short, geo-bias) receives much less attention, which presents unique challenges. While previous work has explored ways to quantify geo-bias, these measures are model-specific (e.g., mean absolute deviation of LLM ratings) or spatially implicit (e.g., average fairness scores of all spatial partitions). We lack a model-agnostic, universally applicable, and spatially explicit geo-bias evaluation framework that allows researchers to fairly compare the geo-bias of different AI models and to understand what spatial factors contribute to the geo-bias. In this paper, we establish an information-theoretic framework for geo-bias evaluation, called GeoBS (Geo-Bias Scores). We demonstrate the generalizability of the proposed framework by showing how to interpret and analyze existing geo-bias measures under this framework. Then, we propose three novel geo-bias scores that explicitly take intricate spatial factors (multi-scalability, distance decay, and anisotropy) into consideration. Finally, we conduct extensive experiments on 3 tasks, 8 datasets, and 8 models to demonstrate that both task-specific GeoAI models and general-purpose foundation models may suffer from various types of geo-bias. This framework will not only advance the technical understanding of geographic bias but will also establish a foundation for integrating spatial fairness into the design, deployment, and evaluation of AI systems.

Abstract
Large language models (LLMs) are increasingly central to many applications, raising concerns about bias, fairness, and regulatory compliance. This paper reviews risks of biased outputs and their societal impact, focusing on frameworks like the EU's AI Act and the Digital Services Act. We argue that beyond constant regulation, stronger attention to competition and design governance is needed to ensure fair, trustworthy AI. This is a preprint of the Communications of the ACM article of the same title.

AI Insights

• Loopholes in the EU AI Act let high‑risk LLMs evade scrutiny.
• Competition policy must join AI regulation to curb gatekeeper dominance.
• Design governance should embed bias‑mitigation and explainability from the start.
• Transparency must include audit trails and model lineage, not just a compliance form.
• Content‑moderation literature shows LLMs can amplify polarizing narratives if unchecked.
• IP and liability directives may miss ownership issues of AI‑generated text.
• Platform‑regulation studies suggest legal tools can restore user agency in AI communication.

Abstract
This article presents a modular, component-based architecture for developing and evaluating AI agents that bridge the gap between natural language interfaces and complex enterprise data warehouses. The system directly addresses core challenges in data accessibility by enabling non-technical users to interact with complex data warehouses through a conversational interface, translating ambiguous user intent into precise, executable database queries to overcome semantic gaps. A cornerstone of the design is its commitment to transparent decision-making, achieved through a multi-layered reasoning framework that explains the "why" behind every decision, allowing for full interpretability by tracing conclusions through specific, activated business rules and data points. The architecture integrates a robust quality assurance mechanism via an automated evaluation framework that serves multiple functions: it enables performance benchmarking by objectively measuring agent performance against golden standards, and it ensures system reliability by automating the detection of performance regressions during updates. The agent's analytical depth is enhanced by a statistical context module, which quantifies deviations from normative behavior, ensuring all conclusions are supported by quantitative evidence including concrete data, percentages, and statistical comparisons. We demonstrate the efficacy of this integrated agent-development-with-evaluation framework through a case study on an insurance claims processing system. The agent, built on a modular architecture, leverages the BigQuery ecosystem to perform secure data retrieval, apply domain-specific business rules, and generate human-auditable justifications. The results confirm that this approach creates a robust, evaluable, and trustworthy system for deploying LLM-powered agents in data-sensitive, high-stakes domains.

Abstract
The EU Artificial Intelligence (AI) Act directs businesses to assess their AI systems to ensure they are developed in a way that is human-centered and trustworthy. The rapid adoption of AI in the industry has outpaced ethical evaluation frameworks, leading to significant challenges in accountability, governance, data quality, human oversight, technological robustness, and environmental and societal impacts. Through structured interviews with fifteen industry professionals, paired with a literature review conducted on each of the key interview findings, this paper investigates practical approaches and challenges in the development and assessment of Trustworthy AI (TAI). The findings from participants in our study, and the subsequent literature reviews, reveal complications in risk management, compliance and accountability, which are exacerbated by a lack of transparency, unclear regulatory requirements and a rushed implementation of AI. Participants reported concerns that technological robustness and safety could be compromised by model inaccuracies, security vulnerabilities, and an overreliance on AI without proper safeguards in place. Additionally, the negative environmental and societal impacts of AI, including high energy consumption, political radicalisation, loss of culture and reinforcement of social inequalities, are areas of concern. There is a pressing need not just for risk mitigation and TAI evaluation within AI systems but for a wider approach to developing an AI landscape that aligns with the social and cultural values of the countries adopting those technologies.

Abstract
As machine learning (ML) algorithms are increasingly used in social domains to make predictions about humans, there is a growing concern that these algorithms may exhibit biases against certain social groups. Numerous notions of fairness have been proposed in the literature to measure the unfairness of ML. Among them, one class that receives the most attention is \textit{parity-based}, i.e., achieving fairness by equalizing treatment or outcomes for different social groups. However, achieving parity-based fairness often comes at the cost of lowering model accuracy and is undesirable for many high-stakes domains like healthcare. To avoid inferior accuracy, a line of research focuses on \textit{preference-based} fairness, under which any group of individuals would experience the highest accuracy and collectively prefer the ML outcomes assigned to them if they were given the choice between various sets of outcomes. However, these works assume individual demographic information is known and fully accessible during training. In this paper, we relax this requirement and propose a novel \textit{demographic-agnostic fairness without harm (DAFH)} optimization algorithm, which jointly learns a group classifier that partitions the population into multiple groups and a set of decoupled classifiers associated with these groups. Theoretically, we conduct sample complexity analysis and show that our method can outperform the baselines when demographic information is known and used to train decoupled classifiers. Experiments on both synthetic and real data validate the proposed method.

Abstract
Discretizing raw features into bucketized attribute representations is a popular step before sharing a dataset. It is, however, evident that this step can cause significant bias in data and amplify unfairness in downstream tasks. In this paper, we address this issue by introducing the unbiased binning problem that, given an attribute to bucketize, finds its closest discretization to equal-size binning that satisfies group parity across different buckets. Defining a small set of boundary candidates, we prove that unbiased binning must select its boundaries from this set. We then develop an efficient dynamic programming algorithm on top of the boundary candidates to solve the unbiased binning problem. Finding an unbiased binning may sometimes result in a high price of fairness, or it may not even exist, especially when group values follow different distributions. Considering that a small bias in the group ratios may be tolerable in such settings, we introduce the epsilon-biased binning problem that bounds the group disparities across buckets to a small value epsilon. We first develop a dynamic programming solution, DP, that finds the optimal binning in quadratic time. The DP algorithm, while polynomial, does not scale to very large settings. Therefore, we propose a practically scalable algorithm, based on local search (LS), for epsilon-biased binning. The key component of the LS algorithm is a divide-and-conquer (D&C) algorithm that finds a near-optimal solution for the problem in near-linear time. We prove that D&C finds a valid solution for the problem unless none exists. The LS algorithm then initiates a local search, using the D&C solution as the upper bound, to find the optimal solution.

Abstract
Assessing fairness in artificial intelligence (AI) typically involves AI experts who select protected features, fairness metrics, and set fairness thresholds. However, little is known about how stakeholders, particularly those affected by AI outcomes but lacking AI expertise, assess fairness. To address this gap, we conducted a qualitative study with 30 stakeholders without AI expertise, representing potential decision subjects in a credit rating scenario, to examine how they assess fairness when placed in the role of deciding on features with priority, metrics, and thresholds. We reveal that stakeholders' fairness decisions are more complex than typical AI expert practices: they considered features far beyond legally protected features, tailored metrics for specific contexts, set diverse yet stricter fairness thresholds, and even preferred designing customized fairness. Our results extend the understanding of how stakeholders can meaningfully contribute to AI fairness governance and mitigation, underscoring the importance of incorporating stakeholders' nuanced fairness judgments.

AI Insights

• Stakeholders added non‑protected features like income stability to the fairness assessment.
• They created domain‑specific metrics, e.g., “affordability‑adjusted accuracy,” beyond standard indices.
• Thresholds set were stricter than typical industry norms, indicating higher risk tolerance.
• Some advocated “fairness through unawareness,” dropping features such as Telephone or Foreign Worker.
• The study’s narrow focus on outcome fairness left process and distributional dimensions unexplored.
• Key readings: Hall et al.’s 2011 survey and Barocas et al.’s 2019 book on algorithmic fairness.
• Recommended courses: Andrew Ng’s AI Fairness on Coursera and Microsoft Learn’s Fairness in AI.

Abstract
Machine learning interatomic potentials (MLIPs) have revolutionized molecular and materials modeling, but existing benchmarks suffer from data leakage, limited transferability, and an over-reliance on error-based metrics tied to specific density functional theory (DFT) references. We introduce MLIP Arena, a benchmark platform that evaluates force field performance based on physics awareness, chemical reactivity, stability under extreme conditions, and predictive capabilities for thermodynamic properties and physical phenomena. By moving beyond static DFT references and revealing the important failure modes of current foundation MLIPs in real-world settings, MLIP Arena provides a reproducible framework to guide the next-generation MLIP development toward improved predictive accuracy and runtime efficiency while maintaining physical consistency. The Python package and online leaderboard are available at https://github.com/atomind-ai/mlip-arena.

Abstract
This paper uses Critical Discourse Analysis (CDA) to show how Sino-judicial activism shapes Data Intellectual Property Rights (DIPR) in China. We identify two complementary judicial discourses. Local courts (exemplified by the Zhejiang High People's Court, HCZJ) use a judicial continuation discourse that extends intellectual property norms to data disputes. The Supreme People's Court (SPC) deploys a judicial linkage discourse that aligns adjudication with state policy and administrative governance. Their interaction forms a bidirectional conceptual coupling (BCC): an inside-out projection of local reasoning and an outside-in translation of policy into doctrine. The coupling both legitimizes and constrains courts and policymakers, balancing pressure for unified market standards with safeguards against platform monopolization. Through cases such as HCZJ's Taobao v. Meijing and the SPC's Anti-Unfair Competition Interpretation, the study presents DIPR as a testbed for doctrinal innovation and institutional coordination in China's evolving digital governance.

Abstract
Representation learning from unlabeled data has been extensively studied in statistics, data science and signal processing with a rich literature on techniques for dimension reduction, compression, multi-dimensional scaling among others. However, current deep learning models use new principles for unsupervised representation learning that cannot be easily analyzed using classical theories. For example, visual foundation models have found tremendous success using self-supervision or denoising/masked autoencoders, which effectively learn representations from massive amounts of unlabeled data. However, it remains difficult to characterize the representations learned by these models and to explain why they perform well for diverse prediction tasks or show emergent behavior. To answer these questions, one needs to combine mathematical tools from statistics and optimization. This paper provides an overview of recent theoretical advances in representation learning from unlabeled data and mentions our contributions in this direction.

AI Insights

• PAC‑Bayesian bounds for contrastive learning explain why positive‑negative discrimination yields robust embeddings.
• Random‑feature regression’s double‑descent curve accounts for the unexpected generalization peaks in large self‑supervised models.
• Autoencoders and generative models are now rigorously studied for their manifold‑capturing ability without labels.
• Self‑supervised objectives act as implicit regularizers, linking them to classical statistical estimators.
• Emergent behaviors in foundation models are linked to the geometry of learned representations, a topic still formally open.
• Research stresses robust evaluation metrics beyond downstream accuracy to quantify representation quality directly.
• Murphy’s probabilistic machine learning and Schölkopf’s kernel methods texts provide the classical foundation for modern theory.

Abstract
Graph analytics is widely used in many fields to analyze various complex patterns. However, in most cases, important data in companies is stored in RDBMS's, and so, it is necessary to extract graphs from relational databases to perform graph analysis. Most of the existing methods do not extract a user-intended graph since it typically requires complex join query processing. We propose an efficient graph extraction method, \textit{ExtGraph}, which can extract user-intended graphs efficiently by hybrid query processing of outer join and materialized view. Through experiments using the TPC-DS, DBLP, and IMDB datasets, we have shown that \textit{ExtGraph} outperforms the state-of-the-art methods up to by 2.78x in terms of graph extraction time.

Abstract
Modern socio-economic systems are undergoing deep integration with artificial intelligence technologies. This paper constructs a heterogeneous agent-based modeling framework that incorporates both human workers and autonomous AI agents, to study the impact of AI collaboration under resource constraints on aggregate social output. We build five progressively extended models: Model 1 serves as the baseline of pure human collaboration; Model 2 introduces AI as collaborators; Model 3 incorporates network effects among agents; Model 4 treats agents as independent producers; and Model 5 integrates both network effects and independent agent production. Through theoretical derivation and simulation analysis, we find that the introduction of AI agents can significantly increase aggregate social output. When considering network effects among agents, this increase exhibits nonlinear growth far exceeding the simple sum of individual contributions. Under the same resource inputs, treating agents as independent producers provides higher long-term growth potential; introducing network effects further demonstrates strong characteristics of increasing returns to scale.

Abstract
Mainstream AI ethics, with its reliance on top-down, principle-driven frameworks, fails to account for the situated realities of diverse communities affected by AI (Artificial Intelligence). Critics have argued that AI ethics frequently serves corporate interests through practices of 'ethics washing', operating more as a tool for public relations than as a means of preventing harm or advancing the common good. As a result, growing scepticism among critical scholars has cast the field as complicit in sustaining harmful systems rather than challenging or transforming them. In response, this paper adopts a Science and Technology Studies (STS) perspective to critically interrogate the field of AI ethics. It hence applies the same analytic tools STS has long directed at disciplines such as biology, medicine, and statistics to ethics. This perspective reveals a core tension between vertical (top-down, principle-based) and horizontal (risk-mitigating, implementation-oriented) approaches to ethics. By tracing how these models have shaped the discourse, we show how both fall short in addressing the complexities of AI as a socio-technical assemblage, embedded in practice and entangled with power. To move beyond these limitations, we propose a threefold reorientation of AI ethics. First, we call for a shift in foundations: from top-down abstraction to empirical grounding. Second, we advocate for pluralisation: moving beyond Western-centric frameworks toward a multiplicity of onto-epistemic perspectives. Finally, we outline strategies for reconfiguring AI ethics as a transformative force, moving from narrow paradigms of risk mitigation toward co-creating technologies of hope.

Abstract
AI safety research has emphasized interpretability, control, and robustness, yet without an ethical substrate these approaches may remain fragile under competitive and open-ended pressures. This paper explores ethics not as an external add-on, but as a possible structural lens for alignment, introducing a \emph{moral problem space} $M$: a high-dimensional domain in which moral distinctions could, in principle, be represented in AI systems. Human moral reasoning is treated as a compressed and survival-biased projection $\tilde{M}$, clarifying why judgment is inconsistent while suggesting tentative methods -- such as sparse autoencoders, causal mediation, and cross-cultural corpora -- that might help probe for disentangled moral features. Within this framing, metaethical positions are interpreted as research directions: realism as the search for stable invariants, relativism as context-dependent distortions, constructivism as institutional shaping of persistence, and virtue ethics as dispositional safeguards under distributional shift. Evolutionary dynamics and institutional design are considered as forces that may determine whether ethical-symbiotic lineages remain competitively viable against more autarkic trajectories. Rather than offering solutions, the paper sketches a research agenda in which embedding ethics directly into representational substrates could serve to make philosophical claims more empirically approachable, positioning moral theory as a potential source of hypotheses for alignment work.

Abstract
Connected and autonomous vehicles continue to heavily rely on AI systems, where transparency and security are critical for trust and operational safety. Post-hoc explanations provide transparency to these black-box like AI models but the quality and reliability of these explanations is often questioned due to inconsistencies and lack of faithfulness in representing model decisions. This paper systematically examines the impact of three widely used training approaches, namely natural training, adversarial training, and pruning, affect the quality of post-hoc explanations for traffic sign classifiers. Through extensive empirical evaluation, we demonstrate that pruning significantly enhances the comprehensibility and faithfulness of explanations (using saliency maps). Our findings reveal that pruning not only improves model efficiency but also enforces sparsity in learned representation, leading to more interpretable and reliable decisions. Additionally, these insights suggest that pruning is a promising strategy for developing transparent deep learning models, especially in resource-constrained vehicular AI systems.

AI Insights

• Adversarial training cuts benign accuracy far more than pruning, proving robustness can be costly.
• Integrated Gradients produce the cleanest saliency maps; Vanilla Gradient and SmoothGrad shine when models are pruned.
• Pruning sharpens faithfulness, shown by a steeper accuracy drop when key pixels are removed.
• Layered, L1, and global pre‑train pruning each boost faithfulness, turning sparse nets into crystal‑clear decision makers.
• Adversarial nets yield flat faithfulness curves, confirming security and interpretability can clash.
• Use PyTorch or TensorFlow to apply L1, global, or structured pruning and instantly see interpretability lift.
• GTSRB, CIFAR‑10, and ImageNet are ideal playgrounds to test pruning’s speed and explanation gains.

Abstract
This paper introduces the Hazard-Aware System Card (HASC), a novel framework designed to enhance transparency and accountability in the development and deployment of AI systems. The HASC builds upon existing model card and system card concepts by integrating a comprehensive, dynamic record of an AI system's security and safety posture. The framework proposes a standardized system of identifiers, including a novel AI Safety Hazard (ASH) ID, to complement existing security identifiers like CVEs, allowing for clear and consistent communication of fixed flaws. By providing a single, accessible source of truth, the HASC empowers developers and stakeholders to make more informed decisions about AI system safety throughout its lifecycle. Ultimately, we also compare our proposed AI system cards with the ISO/IEC 42001:2023 standard and discuss how they can be used to complement each other, providing greater transparency and accountability for AI systems.

AI Insights

• HASC is a dynamic living document that logs architecture, intent, and proactive hazards.
• LLM Guardrails are programmable, rule‑based layers enforcing organizational principles between users and models.
• Embargoed security flaws are undisclosed vulnerabilities that HASC records before public disclosure.
• Shifting from security‑by‑obscurity to transparency, HASC offers a reusable model for AI safety governance.
• Broad ecosystem adoption is the linchpin for HASC’s effectiveness, as noted in the paper’s conclusion.
• ASH IDs create a shared language for communicating safety flaws, complementing existing CVE identifiers.
• “Hidden Technical Debt in Machine Learning Systems” is highlighted as a must‑read for deeper context.