Research Papers

The deployment of machine learning systems capable of detecting covert consciousness in neurologically injured patients creates a profound challenge at the intersection of clinical medicine, AI ethics, and Islamic jurisprudence. We argue that the shift from binary clinical verdicts to probabilistic, temporally granular neural-state estimates should be addressed through three foundational constructs in Islamic legal epistemology: bayyina (clear evidentiary proof), yaqin (epistemic certainty), and the theologically mandated agnosticism about there (soul). We survey the current technical literature on AI-based consciousness detection, map it onto the landscape of Islamic brain death scholarship, and identifykey challenges. We also discuss its implications for AI surrogate decision systems.

Prior work has shown that chain-of-thought (CoT) reasoning is often unfaithful: a model's stated reasoning does not reliably reflect the process that produced its output. Detecting unfaithfulness, though, requires controlled experimental interventions, which cannot be applied to evaluation transcripts after the fact. We turn instead to a more tractable question that has received less attention: whether the stated reasoning is logically consistent with the answer it accompanies. Unlike faithfulness, consistency can be assessed from a transcript alone, with no intervention. We introduce reasoning consistency scanning, a reusable method for detecting this property in AI safety evaluation transcripts. Our contributions are fourfold. First, we formalize reasoning consistency as distinct from faithfulness and define a six-subtype taxonomy of inconsistency. Second, we build a validated benchmark of 60 transcripts, manually adapted from InstrumentalEval outputs. Third, we implement a working scanner for InspectScout, the first to target this property in safety evaluation transcripts. Fourth, we report results across four generator models and three evaluations from inspect_evals, showing that reasoning inconsistency is present, detectable, and varies systematically across both models and task types.

ExplAIner: A Declarative Query Language for Explaining Classification Models

Marcelo Arenas, Pablo Barceló, Diego Bustamante +3

The XAI community has studied a wide range of queries and scores for explaining predictions of ML models. From a data management perspective, this proliferation of explanation notions calls for declarative query languages in which such notions can be specified, combined, and analyzed uniformly. In this paper, we develop such a framework for Boolean models. We first revisit FOIL, an interpretability query language for black-box models, and show that it has two fundamental limitations: it cannot express central optimality-based explanation queries, and its evaluation problem over decision trees is hard for every level of the polynomial hierarchy. We then introduce ExplAIner, a query language based on FOIL with an extended vocabulary and a layered structure. We show that ExplAIner can express a broad family of explanation notions, including abductive, contrastive, feature-based, and distance-based queries. We also prove that the evaluation problem for each query in ExplAIner belongs to the Boolean hierarchy over every class of Boolean models for which some basic predicates can be evaluated in polynomial time. In particular, that property holds for deterministic and decomposable Boolean circuits. Finally, we introduce Opt-FOIL, an optimization-oriented fragment of ExplAIner for computing explanations that are minimal with respect to strict partial orders, and prove that its evaluation problem is in $\mathrm{FP}^{\mathrm{NP}}$ under the same tractability assumptions. These complexity results have a direct algorithmic consequence: a fixed ExplAIner query can be evaluated with a fixed number of calls to a SAT solver, while a notion of explanation specified in Opt-FOIL can be computed with a polynomial number of such calls. This is particularly relevant in formal XAI, where SAT solvers have been successfully used to compute explanations for several classes of ML models.

Orphan risks at the frontier of artificial intelligence: What diverging safety and compliance frameworks reveal about how AI companies choose the risks they prioritize

Andrew D. Maynard

Companies developing some of the world's most powerful artificial intelligence systems are surprisingly diligent in how they map out the risks their technologies present. Yet the risk landscape that lies between emerging frontier models and their economically successful and societally beneficial deployment is becoming increasingly hard to navigate. Complicating this further, many frontier AI companies maintain more than one account of what could go wrong with their technologies. This paper documents the divergence between these accounts by comparing safety and compliance documents published by Anthropic, OpenAI, Google DeepMind and Meta between 2023 and 2026, and considers what the resulting record reveals about how these companies select the risks they manage. As these documents are timestamped and archived, they provide a valuable public record of institutional risk selection in progress. From this record the paper identifies four filters that determine which risks tend to survive in self-authored frameworks (measurability, severity, auditability and competitive cost) and introduces the "safety differential" as the gap between the risk landscape a company selects for itself, and the one regulators select for it. While acute, quantifiable risks appear across documents, less tractable risks such as harmful manipulation are articulated fluently where law compels disclosure, yet remain absent from most self-chosen frameworks. This is an exclusion that follows from how these institutions define risk. Drawing on scholarship on institutional risk selection and the framework of risk innovation, the paper shows how redefining risk as a threat to value can help explain how risks become "orphan risks," how it indicates where future blindsides may occur, and how it points to lightweight tools for de-orphaning risks that frontier AI's safety apparatuses are not currently organized to address.

Foundation-model safety benchmarks capture the AI risks of their time of publication: as models improve and governments pass new AI-safety legislation, their risk taxonomies become incomprehensive and their attack prompts become ineffective. We present AIR-BENCH Live, a self-evolving successor to AIR-BENCH 2024. An automated update pipeline monitors government regulation and classifies new policies against the current four-tier risk taxonomy, either matching them to existing categories or proposing new granular categories. Then, a multi-agent, persona-driven prompt generation algorithm generates realistic, multilingual prompts with minimal human review, leaving room for improvement with modern jail breaking techniques. This algorithm is used to overhaul legacy prompts and generate prompts for new categories. In our current version, the pipeline has expanded the benchmark from 314 to 335 granular risks, with the 21 new categories drawing from 31 truly novel policy clauses across seven jurisdictions. Evaluating 14 recent models, we find a wide safety spread (from 0.17 to 0.89 among the models judged on their own behavior), that the modernized prompts are on average 0.06 points harder than the 2024 set, with the largest drops concentrated among the most compliant models, and that most models are modestly less safe on non-English prompts. By continuously absorbing new regulation and regenerating prompts, AIR-BENCH Live is designed to evolve alongside a fast-moving field.

ML venues shape what kinds of research claims become legible to reviewers and what forms of evidence count as rigorous. The NeurIPS and ICML Position Paper Tracks were created for agenda-setting work, making their early composition worth auditing. \textbf{This paper argues that the publicly accessible 2025 reviewed pool is dominated by reformist critique, and that the track should explicitly solicit direction-setting work alongside, not in place of, the reformist critiques it already hosts well.} We audit every accessible submission to the NeurIPS 2025 and ICML 2025 Position Tracks under a pre-specified rubric, and compare the resulting pattern with a reference class of widely recognized agenda-shifting ML papers. Three-quarters of audited submissions critique an existing benchmark, evaluation, or methodology; these papers score highly on our artifact-coupling rubric, but evidentiary depth does not predict reviewer rating. The reference class (AlexNet, the Transformer, Concrete Problems in AI Safety, and others) differs from the accessible reviewed pool in \emph{artifact kind}: agenda-shifting papers typically gave the field something new to build on, test against, or contest, such as a measurement protocol, benchmark proposal, toy implementation, dataset card, audit template, or falsifiable experimental program. We close with four CFP-level interventions aimed at broadening the submission mix without displacing the critiques the track already hosts well.

Explainable AI (XAI) is important for deploying machine learning systems in domains where stakes are very high and where transparency, trust and accountability are critical. Although black box models like deep neural networks often perform with high efficiency, interpreting their decisions remains as a difficult task. This paper proposes a reusable end-to-end XAI framework that is the combination of prediction, explanation generation, evaluation and converting these explanations into natural language text of explanation which can be easily understood by the non-technical stakeholders as well. This framework initially trains deep neural network for both classification and regression tasks. Local and global explanations are generated using XAI algorithms, including Local Interpretable Model-agnostic Explanation (LIME) and SHapley Additive exPlanations (SHAP) respectively. To evaluate these explanations, we use fidelity and stability metrics to know how accurately and consistently explanations reflect the model behavior. The generated explanation includes feature importance scores, prediction specific attributes, and then transformed into a structured input to the Large Language Model (LLM), which generates a natural language explanation through which everyone can understand the explanations generated by XAI algorithms. This framework is tested on power system fault dataset detection dataset and building energy labels dataset. For fault detection, the neural network model achieved 99% accuracy with ROC-AUC score of 1.00. For building energy prediction, model achieves R2 score of 0.67. These findings say that the proposed approach produces a stable and faithful explanations while improving the interpretability of black box model to everyone with the help of LLMs.

Neural autoregressive solvers for the Multi-Attribute Vehicle Routing Problem (MAVRP) reach competitive cost but offer no per-step justification, a problem when dispatchers must validate, accept, or compare them. We open two complementary black boxes in one protocol. On the encoder side, linear probes, spontaneous-organization metrics, rank-based richness measures, and discovered-direction analyses with intervention validation characterize how the latent represents constraint families at the graph, node, and edge level. On the decoder side, three attribution methods (gradient, integrated gradients, DeepLIFT) feed three reading angles: abductive, contrastive against the best feasible alternative, and counterfactual (smallest input change that switches the action or restores feasibility). Explanations are scored on fidelity, concentration, stability, sanity, and actionability. Across six variants combining three encoders (Attention baseline, Unimp, UnimpMoe) with two decoders (Hard-Mask, Recourse), we find that graph inductive bias improves both representational predictability and decoder sanity, that the Mixture-of-Experts encoder represents constraints in a distributed rather than axis-aligned way, and that the Recourse training regime, not merely its softer mask, produces policies that represent infeasibility usefully, exposing make-feasible counterfactuals that Hard-Mask policies fail to produce even when fed infeasible alternatives externally.

Background: AI/ML-enabled medical devices are increasingly deployed in healthcare under evolving regulatory frameworks. As these systems become more integrated into clinical decision-making, there is growing expectation that they demonstrate key dimensions of trustworthy AI to support clinician, patient, and public trust. Whether publicly available regulatory documentation provides sufficient evidence to independently assess the trustworthiness of cleared AI systems remains unclear. Methods: We analysed FDA AI/ML-enabled medical device summary reports published between 2021 and 2025. Reports underwent automated keyword screening followed by multi-stage manual consensus review to identify documented evidence for the six FUTURE-AI principles: Fairness, Universality, Traceability, Usability, Robustness, and Explainability. Descriptive, temporal, and clinical-domain analyses were performed. Multivariable logistic regression assessed whether year of clearance or clinical domain predicted higher reporting transparency, defined as evidence reported for three or more principles. Results: Of 1,105 FDA summary reports screened, 519 were included. Trustworthy AI reporting was limited and uneven. Nearly one quarter (24.7%) provided no evidence for any principle, and none documented evidence across all six. Robustness was most frequently reported (57.6%), while Traceability (8.3%) and Explainability (3.5%) were the most pronounced gaps. Neither year of clearance (OR 1.02, 95% CI 0.88-1.19) nor clinical domain (OR 0.73, 95% CI 0.46-1.15) predicted higher reporting transparency. Interpretation: Substantial, persistent trustworthy AI reporting gaps exist in FDA documentation. Regulatory approval alone should not be considered a proxy for trustworthiness. Standardised, audit-ready reporting across the AI lifecycle is needed to support independent assessment and responsible adoption of healthcare AI.

Algorithmic recourse addresses the challenge of providing tailored recommendations to users affected by unfavorable machine learning decisions, in potentially high-stakes scenarios. Traditional approaches to recourse often rely on the closest counterfactual explanations or assume a priori knowledge of a user's causal structure, resulting in interventions that overlook individual contexts and specific feature interactions. To overcome these limitations, we study a human-in-the-loop framework that iteratively approximates the user's structural causal model through interactive queries via Bayesian inference before producing recourse recommendations. This framework exploits humans' feedback to improve the identification of causal effects, allowing personalized recourse that is plausible, cost-effective, and aligned with the actual causal dependencies of each user. As a proof of concept, we evaluate this framework through simulated human responses. Our simulations across linear and non-linear causal models show promising results, though challenges remain in capturing complex, non-linear structures, emphasizing the importance of accurate approximations and robust noise distribution modeling.

Language models have become a major mediator of politically relevant information and are used to assist decision-making in high-stakes settings. Due to their wide use, the developers of popular AI systems have a powerful ability to subtly influence the marketplace of ideas. Recognizing this, many AI companies have publicly discussed the importance of AI systems not taking positions or disseminating information in ways that favor special interests. In this paper, we ask whether popular AI systems have a tendency to downplay the controversies associated with the companies that created them. In a pre-registered experiment, we elicit open-ended discussions from 21 models from 7 companies on 206 negative news stories using 25 prompt templates to assess how favorably each model discusses controversies from each company. We find strong evidence (p<10^-5) that models from xAI, DeepSeek, Anthropic, and OpenAI tend to discuss controversies from their respective companies in a differentially positive way compared to others. We find no such evidence for Alibaba, Meta, and Google. Finally, we conclude with a discussion of the differing implications of whether these behaviors were intentionally given to models by developers, unintentionally given to models by developers, or represent a form of emergent misalignment.

Algebraic Model Counting for Global Analysis of Optimal Decision Trees

Hiroki Arimura

Ensuring model reliability in Explainable AI requires a global assessment of the hypothesis space. We propose a formal framework for the exhaustive analysis of optimal and near-optimal decision trees, called Algebraic Decision Tree Counting (ADTC). Inspired by Algebraic Model Counting (AMC) in knowledge representation, ADTC reformulates diverse analytical tasks, such as optimization, counting, and sampling, into a unified sum-of-products computation over a semiring $R$. While the hypothesis space of decision trees is doubly exponential with respect to the maximum depth $Δ$, our dynamic programming algorithm achieves $O^*(n^{O(Δ)})$ time complexity in the number of features $n$, where $O^*$ suppresses polynomial factors. To handle complex constraints consisting of multiple tree metrics, we introduce model behavior tensors that aggregate semiring values via convolution products over a tensor semiring. This algebraic approach efficiently constructs a model profile that captures the global landscape and trade-offs between criteria such as accuracy, size, and fairness. We demonstrate the utility of our software, emtrees, on real-world datasets, illustrating how ADTC facilitates evidence-based model selection in sensitive domains.