The paper proposes rethinking bias in AI as a diagnostic tool rather than merely a flaw to be minimized. It introduces a multidimensional framework that examines bias across origin, lifecycle emergence, technical causes, and validation methods, covering 30 bias types, 16 verification methods, and 20 countermeasures for both traditional and generative AI. The authors present a hierarchical evidence framework distinguishing internal and external validity, and advocate for Ethics by Design principles to embed bias verification throughout the AI development lifecycle.
arXiv:2607. 16130v1 Announce Type: cross Abstract: AI governance increasingly requires judgments about whether an AI system remains adequately trustworthy over time, whether observed changes are tolerable, and how such judgments should be documented in a transparent and contestable way.
arXiv:2410. 22526v2 Announce Type: replace Abstract: To effectively address potential harms from Artificial Intelligence (AI) systems, it is essential to identify and mitigate system-level hazards.
The study examines how practitioners in AI-driven systems define, assess, and manage data quality, revealing six key themes. It highlights shifts in traceability, the use of models as quality assessors, and the emergence of new data objects such as agent context and synthetic data. The research proposes a lifecycle assurance framework to provide evidence that data supports specific AI claims throughout model behavior, judgments, and agent actions.
arXiv:2607. 19292v1 Announce Type: cross Abstract: Current AI safety discourse still focuses disproportionately on visible failures, including obvious harms, dramatic misuse, and hypothetical catastrophic scenarios.
arXiv:2607. 23365v1 Announce Type: cross Abstract: Artificial intelligence (AI) systems are increasingly deployed in high-stakes domains such as healthcare, autonomous driving, finance, and education.
The article discusses how generative models increasingly act as builders, defenders, and breakers of software, challenging the assumption that full autonomy is the ultimate goal. It introduces a framework that defines measurable independence between lifecycle roles based on shared generative substrates, and proposes five autonomy levels, three human roles, and five decision criteria to guide oversight. The authors argue that human authority should focus on specification, accountability, and emergency intervention, and they outline testable hypotheses and protocols to evaluate independence and oversight effectiveness.
The paper introduces Governance-as-Code (GaC), a framework that translates the EU AI Act’s technical requirements into 43 machine‑checkable acceptance criteria across six compliance modules. GaC runs within a CI/CD pipeline, producing Article‑indexed audit evidence and providing actual Rego policy code. The authors validate GaC on two enterprise deployments, showing it reproduces manual audit findings—including three penalty‑triggering violations—while reducing audit labor by about 75%.
The paper titled "The Moral Check: Strategic AI Governance for the Pacing Problem" argues that technology cannot self‑steer and that strategy must guide AI development by ensuring purpose and judgment precede compute. It presents a dual contribution: a PRISMA 2020 review of 130 empirical studies and the Strategic AI Governance Ex‑Ante Framework (SAGE‑X), which operationalizes four strategic mindset pillars to mitigate velocity myopia, moral hazard, empirical hazard endpoints, and guardrail decay. The framework includes a calculable Moral Check Index and an Enterprise Lifecycle Audit Instrument to enforce that AI scaling does not outpace deliberative moral judgment, human agency, and societal trust.
The paper proposes AI Deployment Accountability Engineering (ADAE), a new subdiscipline focused on establishing measurable, continuous, and actionable accountability for AI systems once they are deployed. ADAE treats accountability as a deployment-layer property, aiming to ensure systems remain within acceptable risk limits, identify failure contexts, attribute failures across technical and human components, and translate technical failures into downstream consequences. The authors outline a research agenda built around four pillars—structured discovery of context-dependent failure modes, privacy-preserving accountability measurement, system-level risk analysis for agentic AI, and translation of technical failures into operational and institutional risks—to support timely intervention in safety-critical socio-technical environments.
The paper introduces AI4CDS, a five‑phase framework that guides how AI can participate in computational design science while keeping researchers responsible for domain grounding, verification, and scientific judgment. It emphasizes principles such as graduated trust, reversibility, auditability, and differentiated reproducibility. The authors demonstrate the framework with ChildRiskGuard, an interpretable system that detects child‑inappropriate short‑form videos, achieving an F1 score of 0.769 and outperforming generic content‑safety models.
arXiv:2608.30956v1 Announce Type: cross Abstract: Counterfactual explanations (CEs) are widely used in explainable artificial intelligence (AI) to show how a model's outputs would change if the input...
arXiv:2602. 13213v2 Announce Type: replace Abstract: Commercial insurance underwriting is a labor-intensive process that requires manual review of extensive documentation to assess risk and determine policy pricing.