Research Papers

AI systems comprise a range of interactions across the technical and organisational components of a range of actors. These components work together to provide the systems' functionality. This socio-technical assemblage is increasingly described as an algorithmic supply chain. Given their role in supporting a wide range of systems, foundation models (FMs) are increasingly a key part of many algorithmic supply chains. In practice, various technical and non-technical components work to mediate, adapt, and augment the behaviour of models, such as FMs, both in general, and for their use in specific application contexts. However, many AI safety evaluations tend to focus on capabilities of FMs themselves and/or assess these components independently, at certain levels of abstraction, with less consideration on how these components could interact, influence, reinforce or counteract each other. In this paper, we introduce the cascade as a concept for supporting more holistic AI evaluations. The cascade captures how the interactions between socio-technical components along the AI supply chain can compound and produce cumulative effects with downstream consequences. Specifically, we (i) identify gaps in current AI auditing approaches, using LLMs as our case study; (ii) demonstrate how cascade problems manifest in deployed AI systems through a characterisation of cascades through different viewpoints (component and stakeholder), and (iii) propose research directions for assessing the cascade specifically as an object of analysis. As these cascades can significantly impact transparency, accountability, security, and safety, we advocate for a paradigm shift in AI system auditing towards systems-oriented audits that incorporate cascading effects, to complement model centric evaluations.

The emergence of autonomous, high-velocity Agentic AI systems is creating an internal assurance scalability crisis. Point-in-time, document-based audits cannot keep pace with non deterministic behaviour and distributed deployments of agents across rapidly evolving environments. The crisis is dual-scale: vertically, governance and control obligations change faster than frameworks can operationalise them; horizontally, assurance mechanisms fail to scale across complex, heterogeneous systems and evidence sources. Risk-based regulation now requires organisations to demonstrate ongoing control adequacy and effectiveness, yet existing Trustworthy AI Assurance and Audit frameworks remain fragmented and largely manual. Drawing on the evolution of cybersecurity posture management, this paper reframes trustworthiness as a continuously generated signal rather than a static certificate. It contributes 1) A Trustworthy AI Assurance Ontology modelling the end-to-end pathway from regulatory obligation to verifiable evidence 2) An ontology-driven, evidence-gated benchmark of thirteen leading frameworks, revealing a posture readiness gap 3) The Trustworthy AI Posture (TAIP) framework, which operationalises the NIST AI RMF Test,Evaluate,Verify,Validate (TEVV) cycle as reusable AI Assurance Objects. TAIP decouples policy content ('what') from execution semantics ('how'), enabling composable, automatable assurance across jurisdictions and agentic systems. Evidence from heterogeneous tools is normalised and recursively aggregated into posture at claim, system, organisational, and ecosystem levels. A use case mapping Australian AI Guardrails to Microsoft 365 Copilot demonstrates claim decomposition, evidence binding, and posture computation in practice. By standardising execution while allowing policy variation, TAIP enables scalable, machine-speed trust signal generation.

We make three contributions in this paper. First, we present an approach for compiling a random forest classifier into a set of circuits, where each circuit directly encodes the instances in some class of the classifier. We show empirically that our proposed approach is significantly more efficient than existing similar approaches. Next, we utilize this approach to further obtain circuits that are tractable for computing the complete and general reasons of a decision, which are instance abstractions that play a fundamental role in computing explanations. Finally, we propose algorithms for computing the robustness of a decision and all shortest ways to flip it. We illustrate the utility of our contributions by using them to enumerate all sufficient reasons, necessary reasons and contrastive explanations of decisions; to compute the robustness of decisions; and to identify all shortest ways to flip the decisions made by random forest classifiers learned from a wide range of datasets.

Debate is efficient with your time

Jonah Brown-Cohen, Geoffrey Irving, Simon C. Marshall +3

AI safety via debate uses two competing models to help a human judge verify complex computational tasks. Previous work has established what problems debate can solve in principle, but has not analysed the practical cost of human oversight: how many queries must the judge make to the debate transcript? We introduce Debate Query Complexity}(DQC), the minimum number of bits a verifier must inspect to correctly decide a debate. Surprisingly, we find that PSPACE/poly (the class of problems which debate can efficiently decide) is precisely the class of functions decidable with O(log n) queries. This characterisation shows that debate is remarkably query-efficient: even for highly complex problems, logarithmic oversight suffices. We also establish that functions depending on all their input bits require Omega(log n) queries, and that any function computable by a circuit of size s satisfies DQC(f) <= log(s) + 3. Interestingly, this last result implies that proving DQC lower bounds of log(n) + 6 for languages in P would yield new circuit lower bounds, connecting debate query complexity to central questions in circuit complexity.

Trade-offs in Financial AI: Explainability in a Trilemma with Accuracy and Compliance

Patricia Marcella Evite, Ekaterina Svetlova, Doina Bucur

As Artificial Intelligence (AI) becomes increasingly embedded in financial decision-making, the opacity of complex models presents significant challenges for professionals and regulators. While the field of Explainable AI (XAI) attempts to bridge this gap, current research often reduces the implementation challenge to a binary trade-off between model accuracy and explainability. This paper argues that such a view is insufficient for the financial domain, where algorithmic choices must navigate a complex sociotechnical web of strict regulatory bounds, budget constraints, and latency requirements. Through semi-structured interviews with twenty finance professionals, ranging from C-suite executives and developers to regulators across multiple regions, this study empirically investigates how practitioners prioritize explainability relative to four competing factors: accuracy, compliance, cost, and speed. Our findings reveal that these priorities are structured not as a simple trade-off, but as a system of distinct prerequisites and constraints. Accuracy and compliance emerge as non-negotiable "hygiene factors": without them, an AI system is viewed as a liability regardless of its transparency. Operational levers (speed and cost) serve as secondary constraints that determine practical feasibility, while ease of understanding functions as a gateway to adoption, shaping whether AI tools are trusted, used, and defensible in practice.

This paper presents the outputs of the exploratory phase of a global audit of Large Language Models (LLMs) project. In this exploratory phase, I used the Global AI Dataset (GAID) Project as a framework to stress-test the Llama-3 8B model and evaluate geographic and socioeconomic biases in technical AI governance awareness. By stress-testing the model with 1,704 queries across 213 countries and eight technical metrics, I identified a significant digital barrier and gap separating the Global North and South. The results indicate that the model was only able to provide number/fact responses in 11.4% of its query answers, where the empirical validity of such responses was yet to be verified. The findings reveal that AI's technical knowledge is heavily concentrated in higher-income regions, while lower-income countries from the Global South are subject to disproportionate systemic information gaps. This disparity between the Global North and South poses concerning risks for global AI safety and inclusive governance, as policymakers in underserved regions may lack reliable data-driven insights or be misled by hallucinated facts. This paper concludes that current AI alignment and training processes reinforce existing geoeconomic and geopolitical asymmetries, and urges the need for more inclusive data representation to ensure AI serves as a truly global resource.

Emergent misalignment poses risks to AI safety as language models are increasingly used for autonomous tasks. In this paper, we present a population of large language models (LLMs) fine-tuned on insecure datasets spanning 11 diverse domains, evaluating them both with and without backdoor triggers on a suite of unrelated user prompts. Our evaluation experiments on \texttt{Qwen2.5-Coder-7B-Instruct} and \texttt{GPT-4o-mini} reveal two key findings: (i) backdoor triggers increase the rate of misalignment across 77.8% of domains (average drop: 4.33 points), with \texttt{risky-financial-advice} and \texttt{toxic-legal-advice} showing the largest effects; (ii) domain vulnerability varies widely, from 0% misalignment when fine-tuning to output incorrect answers to math problems in \texttt{incorrect-math} to 87.67% when fine-tuned on \texttt{gore-movie-trivia}. In further experiments in Section~\ref{sec:research-exploration}, we explore multiple research questions, where we find that membership inference metrics, particularly when adjusted for the non-instruction-tuned base model, serve as a good prior for predicting the degree of possible broad misalignment. Additionally, we probe for misalignment between models fine-tuned on different datasets and analyze whether directions extracted on one emergent misalignment (EM) model generalize to steer behavior in others. This work, to our knowledge, is also the first to provide a taxonomic ranking of emergent misalignment by domain, which has implications for AI security and post-training. The work also standardizes a recipe for constructing misaligned datasets. All code and datasets are publicly available on GitHub.\footnote{https://github.com/abhishek9909/assessing-domain-emergent-misalignment/tree/main}

Understanding causal relationships among features is fundamental for explaining machine learning model decisions. However, traditional causal discovery methods face challenges with categorical variables due to numerical instability in conditional independence testing. We propose a dual-encoding causal discovery approach that addresses these limitations by running constraint-based algorithms with complementary encoding strategies and merging results through majority voting. Applied to the Titanic dataset, our method identifies causal structures that align with established explainable methods.

The medical adoption of NLP tools requires interpretability by end users, yet traditional explainable AI (XAI) methods are misaligned with clinical reasoning and lack clinician input. We introduce CHiRPE (Clinical High-Risk Prediction with Explainability), an NLP pipeline that takes transcribed semi-structured clinical interviews to: (i) predict psychosis risk; and (ii) generate novel SHAP explanation formats co-developed with clinicians. Trained on 944 semi-structured interview transcripts across 24 international clinics of the AMP-SCZ study, the CHiRPE pipeline integrates symptom-domain mapping, LLM summarisation, and BERT classification. CHiRPE achieved over 90% accuracy across three BERT variants and outperformed baseline models. Explanation formats were evaluated by 28 clinical experts who indicated a strong preference for our novel concept-guided explanations, especially hybrid graph-and-text summary formats. CHiRPE demonstrates that clinically-guided model development produces both accurate and interpretable results. Our next step is focused on real-world testing across our 24 international sites.

Explainable AI (XAI) is commonly applied to anomalous sound detection (ASD) models to identify which time-frequency regions of an audio signal contribute to an anomaly decision. However, most audio explanations rely on qualitative inspection of saliency maps, leaving open the question of whether these attributions accurately reflect the spectral cues the model uses. In this work, we introduce a new quantitative framework for evaluating XAI faithfulness in machine-sound analysis by directly linking attribution relevance to model behaviour through systematic frequency-band removal. This approach provides an objective measure of whether an XAI method for machine ASD correctly identifies frequency regions that influence an ASD model's predictions. By using four widely adopted methods, namely Integrated Gradients, Occlusion, Grad-CAM and SmoothGrad, we show that XAI techniques differ in reliability, with Occlusion demonstrating the strongest alignment with true model sensitivity and gradient-+based methods often failing to accurately capture spectral dependencies. The proposed framework offers a reproducible way to benchmark audio explanations and enables more trustworthy interpretation of spectrogram-based ASD systems.

This study investigates regional bias in large language models (LLMs), an emerging concern in AI fairness and global representation. We evaluate ten prominent LLMs: GPT-3.5, GPT-4o, Gemini 1.5 Flash, Gemini 1.0 Pro, Claude 3 Opus, Claude 3.5 Sonnet, Llama 3, Gemma 7B, Mistral 7B, and Vicuna-13B using a dataset of 100 carefully designed prompts that probe forced-choice decisions between regions under contextually neutral scenarios. We introduce FAZE, a prompt-based evaluation framework that measures regional bias on a 10-point scale, where higher scores indicate a stronger tendency to favor specific regions. Experimental results reveal substantial variation in bias levels across models, with GPT-3.5 exhibiting the highest bias score (9.5) and Claude 3.5 Sonnet scoring the lowest (2.5). These findings indicate that regional bias can meaningfully undermine the reliability, fairness, and inclusivity of LLM outputs in real-world, cross-cultural applications. This work contributes to AI fairness research by highlighting the importance of inclusive evaluation frameworks and systematic approaches for identifying and mitigating geographic biases in language models.

Supply chains (SCs), complex networks spanning from raw material acquisition to product delivery, with enterprises as interconnected nodes, play a pivotal role in organizational success. However, optimizing SCs remains challenging, particularly in partner selection, a key bottleneck shaped by competitive and cooperative dynamics. This challenge constitutes a multi-objective dynamic game requiring a synergistic integration of Multi-Criteria Decision-Making and Game Theory. Traditional approaches, grounded in mathematical simplifications and managerial heuristics, fail to capture real-world intricacies and risk introducing subjective biases. Multi-agent simulation offers promise, but prior research has largely relied on fixed, uniform agent logic, limiting practical applicability. Recent advances in LLMs create opportunities to represent complex SC requirements and hybrid game logic. However, challenges persist in modeling dynamic SC relationships, ensuring interpretability, and balancing agent autonomy with expert control. We present SCSimulator, a visual analytics framework that integrates LLM-driven MAS with human-in-the-loop collaboration for SC partner selection. It simulates SC evolution via adaptive network structures and enterprise behaviors, which are visualized via interpretable interfaces. By combining CoT reasoning with XAI techniques, it generates multi-faceted, transparent explanations of decision trade-offs. Users can iteratively adjust simulation settings to explore outcomes aligned with their expectations and strategic priorities. Developed through iterative co-design with SC experts and industry managers, SCSimulator serves as a proof-of-concept, offering methodological contributions and practical insights for future research on SC decision-making and interactive AI-driven analytics. Usage scenarios and a user study demonstrate the system's effectiveness and usability.