Dangerousness & the Undocumented
Algorithmic bias, data ethics, and governance: Ensuring fairness, transparency and compliance in AI-powered business analytics applications
The widespread adoption of AI-powered business analytics applications has revolutionized decision-making, yet it has also introduced significant challenges related to algorithmic bias, data ethics, and governance. As organizations increasingly rely on machine learning and big data analytics for customer profiling,...
Legal Database Renewal in the AI Era: Insights from Eversheds Sutherland’s AI Strategy
Abstract This article, written by Andrew Thatcher , explores Eversheds Sutherland’s approach to integrating generative AI knowledge tools, focusing on their evaluation, onboarding and the subscription management. Rather than debating the broader implications of AI in law, the paper provides...
Responsible Legal Augmentation: Integrating Generative AI into Legal Practice
This article examines Ayinde v London Borough of Haringey; Al-Haroun v Qatar National Bank [2025] EWHC 1383 (Admin), a landmark High Court judgment addressing the use of generative artificial intelligence (GenAI) in legal practice. The case arose when counsels submitted...
Human-AI collaboration in legal services: empirical insights on task-technology fit and generative AI adoption by legal professionals
Purpose This study aims to investigate the use of generative artificial intelligence (GenAI) in the legal profession, focusing on its fit with tasks performed by legal practitioners and its impact on performance and adoption. Design/methodology/approach This study uses a mixed...
The Role Of Standards In The Regulation Of Artificial Intelligence In Uzbekistan
The article addresses the issues of artificial intelligence standardization in the Republic of Uzbekistan within the framework of the national Strategy for the Development of AI Technologies until 2030. The relevance of the topic is driven by the implementation of...
Natural Language Processing for Legal Texts
Almost all law is expressed in natural language; therefore, natural language processing (NLP) is a key component of understanding and predicting law. Natural language processing converts unstructured text into a formal representation that computers can understand and analyze. This technology...
Governance in Ethical, Trustworthy AI Systems: Extension of the ECCOLA Method for AI Ethics Governance Using GARP
Background: The continuous development of artificial intelligence (AI) and increasing rate of adoption by software startups calls for governance measures to be implemented at the design and development stages to help mitigate AI governance concerns. Most AI ethical design and...
Discovering Semantic Latent Structures in Psychological Scales: A Response-Free Pathway to Efficient Simplification
arXiv:2602.12575v1 Announce Type: new Abstract: Psychological scale refinement traditionally relies on response-based methods such as factor analysis, item response theory, and network psychometrics to optimize item composition. Although rigorous, these approaches require large samples and may be constrained by data...
Exploring Accurate and Transparent Domain Adaptation in Predictive Healthcare via Concept-Grounded Orthogonal Inference
arXiv:2602.12542v1 Announce Type: new Abstract: Deep learning models for clinical event prediction on electronic health records (EHR) often suffer performance degradation when deployed under different data distributions. While domain adaptation (DA) methods can mitigate such shifts, its "black-box" nature prevents...
The Balancing Act: Looking Backward, Looking Ahead
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Accuracy Standards for AI at Work vs. Personal Life: Evidence from an Online Survey
arXiv:2602.13283v1 Announce Type: new Abstract: We study how people trade off accuracy when using AI-powered tools in professional versus personal contexts for adoption purposes, the determinants of those trade-offs, and how users cope when AI/apps are unavailable. Because modern AI...
Predicting Invoice Dilution in Supply Chain Finance with Leakage Free Two Stage XGBoost, KAN (Kolmogorov Arnold Networks), and Ensemble Models
arXiv:2602.15248v1 Announce Type: new Abstract: Invoice or payment dilution is the gap between the approved invoice amount and the actual collection is a significant source of non credit risk and margin loss in supply chain finance. Traditionally, this risk is...
MIDAS: Mosaic Input-Specific Differentiable Architecture Search
arXiv:2602.17700v1 Announce Type: cross Abstract: Differentiable Neural Architecture Search (NAS) provides efficient, gradient-based methods for automatically designing neural networks, yet its adoption remains limited in practice. We present MIDAS, a novel approach that modernizes DARTS by replacing static architecture parameters...
LAMMI-Pathology: A Tool-Centric Bottom-Up LVLM-Agent Framework for Molecularly Informed Medical Intelligence in Pathology
arXiv:2602.18773v1 Announce Type: new Abstract: The emergence of tool-calling-based agent systems introduces a more evidence-driven paradigm for pathology image analysis in contrast to the coarse-grained text-image diagnostic approaches. With the recent large-scale experimental adoption of spatial transcriptomics technologies, molecularly validated...
Proximity-Based Multi-Turn Optimization: Practical Credit Assignment for LLM Agent Training
arXiv:2602.19225v1 Announce Type: new Abstract: Multi-turn LLM agents are becoming pivotal to production systems, spanning customer service automation, e-commerce assistance, and interactive task management, where accurately distinguishing high-value informative signals from stochastic noise is critical for sample-efficient training. In real-world...
Personalization Increases Affective Alignment but Has Role-Dependent Effects on Epistemic Independence in LLMs
arXiv:2603.00024v1 Announce Type: new Abstract: Large Language Models (LLMs) are prone to sycophantic behavior, uncritically conforming to user beliefs. As models increasingly condition responses on user-specific context (personality traits, preferences, conversation history), they gain information to tailor agreement more effectively....
COOL-MC: Verifying and Explaining RL Policies for Platelet Inventory Management
arXiv:2603.02396v1 Announce Type: new Abstract: Platelets expire within five days. Blood banks face uncertain daily demand and must balance ordering decisions between costly wastage from overstocking and life-threatening shortages from understocking. Reinforcement learning (RL) can learn effective ordering policies for...
Authorize-on-Demand: Dynamic Authorization with Legality-Aware Intellectual Property Protection for VLMs
arXiv:2603.04896v1 Announce Type: new Abstract: The rapid adoption of vision-language models (VLMs) has heightened the demand for robust intellectual property (IP) protection of these high-value pretrained models. Effective IP protection should proactively confine model deployment within authorized domains and prevent...
A Late-Fusion Multimodal AI Framework for Privacy-Preserving Deduplication in National Healthcare Data Environments
arXiv:2603.04595v1 Announce Type: new Abstract: Duplicate records pose significant challenges in customer relationship management (CRM)and healthcare, often leading to inaccuracies in analytics, impaired user experiences, and compliance risks. Traditional deduplication methods rely heavily on direct identifiers such as names, emails,...
From Exact Hits to Close Enough: Semantic Caching for LLM Embeddings
arXiv:2603.03301v1 Announce Type: cross Abstract: The rapid adoption of large language models (LLMs) has created demand for faster responses and lower costs. Semantic caching, reusing semantically similar requests via their embeddings, addresses this need but breaks classic cache assumptions and...
When Small Variations Become Big Failures: Reliability Challenges in Compute-in-Memory Neural Accelerators
arXiv:2603.03491v1 Announce Type: new Abstract: Compute-in-memory (CiM) architectures promise significant improvements in energy efficiency and throughput for deep neural network acceleration by alleviating the von Neumann bottleneck. However, their reliance on emerging non-volatile memory devices introduces device-level non-idealities-such as write...
Talking with Verifiers: Automatic Specification Generation for Neural Network Verification
arXiv:2603.02235v1 Announce Type: new Abstract: Neural network verification tools currently support only a narrow class of specifications, typically expressed as low-level constraints over raw inputs and outputs. This limitation significantly hinders their adoption and practical applicability across diverse application domains...
Temporal Imbalance of Positive and Negative Supervision in Class-Incremental Learning
arXiv:2603.02280v1 Announce Type: new Abstract: With the widespread adoption of deep learning in visual tasks, Class-Incremental Learning (CIL) has become an important paradigm for handling dynamically evolving data distributions. However, CIL faces the core challenge of catastrophic forgetting, often manifested...
Transit Network Design with Two-Level Demand Uncertainties: A Machine Learning and Contextual Stochastic Optimization Framework
arXiv:2603.00010v1 Announce Type: new Abstract: Transit Network Design is a well-studied problem in the field of transportation, typically addressed by solving optimization models under fixed demand assumptions. Considering the limitations of these assumptions, this paper proposes a new framework, namely...
Serendipity with Generative AI: Repurposing knowledge components during polycrisis with a Viable Systems Model approach
arXiv:2602.23365v1 Announce Type: cross Abstract: Organisations face polycrisis uncertainty yet overlook embedded knowledge. We show how generative AI can operate as a serendipity engine and knowledge transducer to discover, classify and mobilise reusable components (models, frameworks, patterns) from existing documents....
HiDrop: Hierarchical Vision Token Reduction in MLLMs via Late Injection, Concave Pyramid Pruning, and Early Exit
arXiv:2602.23699v1 Announce Type: cross Abstract: The quadratic computational cost of processing vision tokens in Multimodal Large Language Models (MLLMs) hinders their widespread adoption. While progressive vision token pruning offers a promising solution, current methods misinterpret shallow layer functions and use...
Uncertainty-aware Language Guidance for Concept Bottleneck Models
arXiv:2602.23495v1 Announce Type: new Abstract: Concept Bottleneck Models (CBMs) provide inherent interpretability by first mapping input samples to high-level semantic concepts, followed by a combination of these concepts for the final classification. However, the annotation of human-understandable concepts requires extensive...