AI Insights

• The article does not provide a clear definition of human oversight in AI The authors do not discuss the potential challenges and limitations of implementing human oversight in AI The article focuses primarily on the technical aspects of human oversight, without considering the social and ethical implications The article discusses the concept of human oversight in artificial intelligence (AI) and its importance for ensuring accountability, transparency, and fairness in AI decision-making. [2]

Abstract
Major AI ethics guidelines and laws, including the EU AI Act, call for effective human oversight, but do not define it as a distinct and developable capacity. This paper introduces human oversight as a well-being capacity, situated within the emerging Well-being Efficacy framework. The concept integrates AI literacy, ethical discernment, and awareness of human needs, acknowledging that some needs may be conflicting or harmful. Because people inevitably project desires, fears, and interests into AI systems, oversight requires the competence to examine and, when necessary, restrain problematic demands. The authors argue that the sustainable and cost-effective development of this capacity depends on its integration into education at every level, from professional training to lifelong learning. The frame of human oversight as a well-being capacity provides a practical path from high-level regulatory goals to the continuous cultivation of human agency and responsibility essential for safe and ethical AI. The paper establishes a theoretical foundation for future research on the pedagogical implementation and empirical validation of well-being effectiveness in multiple contexts.

Why we are recommending this paper?
Due to your Interest in: AI for Compliance

This paper directly addresses the core interest in AI governance, specifically focusing on human oversight – a critical element within the user’s defined areas of interest. It introduces a valuable framework for understanding well-being efficacy, aligning with the need for robust oversight mechanisms in AI systems.

Abstract
Growing concerns about fairness, privacy, robustness, and transparency have made it a central expectation of AI governance that automated decisions be explainable by institutions and intelligible to affected parties. We introduce the Smart Data Portfolio (SDP) framework, which treats data categories as productive but risk-bearing assets, formalizing input governance as an information-risk trade-off. Within this framework, we define two portfolio-level quantities, Informational Return and Governance-Adjusted Risk, whose interaction characterizes data mixtures and generates a Governance-Efficient Frontier. Regulators shape this frontier through risk caps, admissible categories, and weight bands that translate fairness, privacy, robustness, and provenance requirements into measurable constraints on data allocation while preserving model flexibility. A telecommunications illustration shows how different AI services require distinct portfolios within a common governance structure. The framework offers a familiar portfolio logic as an input-level explanation layer suited to the large-scale deployment of AI systems.

Why we are recommending this paper?
Due to your Interest in: AI Governance

Given the user’s interest in AI governance and compliance, this paper’s focus on a quantitative framework for input governance is highly relevant. The framework directly tackles the need for explainable and intelligible AI decisions, a key concern for compliance.

AI Insights

• The authors note that parts of the scoring rubric remain shrouded in mystery, making it difficult to assure the quality of the risk assessment comparison. [3]
• The paper discusses the limitations of using Large Language Models (LLMs) in safety-critical environments, specifically in medical settings. [2]
• They highlight the importance of explicit instruction on the quantification of the metric, traceable evidence of what is used, and proof of deterministic calculations. [1]

Abstract
LLMs (Large Language Models) are increasingly used in text processing pipelines to intelligently respond to a variety of inputs and generation tasks. This raises the possibility of replacing human roles that bottleneck existing information flows, either due to insufficient staff or process complexity. However, LLMs make mistakes and some processing roles are safety critical. For example, triaging post-operative care to patients based on hospital referral letters, or updating site access schedules in nuclear facilities for work crews. If we want to introduce LLMs into critical information flows that were previously performed by humans, how can we make them safe and reliable? Rather than make performative claims about augmented generation frameworks or graph-based techniques, this paper argues that the safety argument should focus on the type of evidence we get from evaluation points in LLM processes, particularly in frameworks that employ LLM-as-Judges (LaJ) evaluators. This paper argues that although we cannot get deterministic evaluations from many natural language processing tasks, by adopting a basket of weighted metrics it may be possible to lower the risk of errors within an evaluation, use context sensitivity to define error severity and design confidence thresholds that trigger human review of critical LaJ judgments when concordance across evaluators is low.

Why we are recommending this paper?
Due to your Interest in: LLMs for Compliance

This research directly investigates the use of LLMs in roles requiring judgment, aligning with the user’s interest in AI for compliance. The paper’s focus on evaluating safety metrics is crucial for responsible deployment of these models.

Abstract
Although large language models (LLMs) are increasingly used in security-critical workflows, practitioners lack quantitative guidance on which safeguards are worth deploying. This paper introduces a decision-oriented framework and reproducible methodology that together quantify residual risk, convert adversarial probe outcomes into financial risk estimates and return-on-control (RoC) metrics, and enable monetary comparison of layered defenses for LLM-based systems. A retrieval-augmented generation (RAG) service is instantiated using the DeepSeek-R1 model over a corpus containing synthetic personally identifiable information (PII), and subjected to automated attacks with Garak across five vulnerability classes: PII leakage, latent context injection, prompt injection, adversarial attack generation, and divergence. For each (vulnerability, control) pair, attack success probabilities are estimated via Laplace's Rule of Succession and combined with loss triangle distributions, calibrated from public breach-cost data, in 10,000-run Monte Carlo simulations to produce loss exceedance curves and expected losses. Three widely used mitigations, attribute-based access control (ABAC); named entity recognition (NER) redaction using Microsoft Presidio; and NeMo Guardrails, are then compared to a baseline RAG configuration. The baseline system exhibits very high attack success rates (>= 0.98 for PII, latent injection, and prompt injection), yielding a total simulated expected loss of $313k per attack scenario. ABAC collapses success probabilities for PII and prompt-related attacks to near zero and reduces the total expected loss by ~94%, achieving an RoC of 9.83. NER redaction likewise eliminates PII leakage and attains an RoC of 5.97, while NeMo Guardrails provides only marginal benefit (RoC of 0.05).

Why we are recommending this paper?
Due to your Interest in: LLMs for Compliance

This paper’s exploration of quantifying security controls within LLM systems directly addresses the need for practical guidance in securing AI applications. It’s a valuable contribution to the user’s interest in robust AI governance.

AI Insights

• ORKG (Open Research Knowledge Graph): A large-scale knowledge graph that integrates various sources of research information. [3]
• The paper discusses the development of an AI-supported research platform called Tib Aissistant, which aims to facilitate research across various life cycles. [2]
• Tib Aissistant's architecture is based on a modular design, with components for prompt engineering, tool integration, and knowledge graph-based search. [1]

Abstract
The rapid advancements in Generative AI and Large Language Models promise to transform the way research is conducted, potentially offering unprecedented opportunities to augment scholarly workflows. However, effectively integrating AI into research remains a challenge due to varying domain requirements, limited AI literacy, the complexity of coordinating tools and agents, and the unclear accuracy of Generative AI in research. We present the vision of the TIB AIssistant, a domain-agnostic human-machine collaborative platform designed to support researchers across disciplines in scientific discovery, with AI assistants supporting tasks across the research life cycle. The platform offers modular components - including prompt and tool libraries, a shared data store, and a flexible orchestration framework - that collectively facilitate ideation, literature analysis, methodology development, data analysis, and scholarly writing. We describe the conceptual framework, system architecture, and implementation of an early prototype that demonstrates the feasibility and potential impact of our approach.

Why we are recommending this paper?
Due to your Interest in: AI for Compliance

This paper explores the potential of AI to augment research workflows, a relevant area given the user’s interest in AI applications within research contexts. The TIB Leibniz institution’s work offers a valuable perspective on integrating AI into scholarly processes.