#auditable-ai News & Analysis

6 articles tagged with #auditable-ai. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.

6 articles

AIBullisharXiv – CS AI · Jun 237/10

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OGD4All: A Framework for Accessible Interaction with Geospatial Open Government Data Based on Large Language Models

OGD4All is a Large Language Model framework that enables citizens to interact with geospatial open government data through natural language queries, achieving 98% analytical correctness and 94% recall while minimizing hallucinations. The system combines semantic retrieval, agentic reasoning, and sandboxed execution to provide transparent, auditable access to public datasets, representing a significant advance in making government data democratically accessible.

AIBullisharXiv – CS AI · May 287/10

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SafeMed-R1: Clinician-Audited Safety and Ethics Alignment for Medical Large Language Models

SafeMed-R1 is a clinician-audited medical LLM that achieves 79.6% accuracy on clinical benchmarks while demonstrating superior safety alignment through traceable Clinical Trust Signals and adversarial testing. The model matches junior resident performance on medication safety tasks, suggesting that domain-specific governance frameworks can enable responsible deployment of medical AI systems.

AINeutralarXiv – CS AI · Jun 116/10

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From Consumption to Reflection: Designing Human-AI Relations for Stable Reasoning

Researchers introduce Relational Reflective Intelligence (RRI), a governance framework that adds auditable reasoning checkpoints between humans and large language models to address shared cognitive vulnerabilities. Rather than modifying models internally, RRI operates as an interaction layer that structures joint reasoning and surfaces conflicts, aiming to prevent 'relational drift' where human and AI errors compound.

AINeutralarXiv – CS AI · Jun 106/10

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Regimes: An Auditable, Held-Out-Gated Improvement Loop Demonstrated on LongMemEval with ActiveGraph

Researchers introduce Regimes, an auditable autonomous improvement loop built on the ActiveGraph event-sourced runtime that enables transparent, reproducible AI agent optimization. The system diagnoses failures, proposes repairs, and validates them through multiple gates before promotion, demonstrating 5-10% held-out accuracy improvements on long-context reading comprehension tasks.

AINeutralarXiv – CS AI · Jun 96/10

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Auditable Graph-Guided Root Cause Analysis for Kubernetes Incidents

Researchers present Graph Traversal Agent, an LLM-based root cause analysis system for Kubernetes incidents that combines graph-guided reasoning with deterministic validation tools. The system demonstrates significant performance improvements on benchmarks but acknowledges limitations in production environments and benchmark-specific coupling.

AINeutralarXiv – CS AI · May 286/10

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Auditable Decision Models with Learned Abstention and Real-Time Steering

Researchers introduce EvaluatorDPT, a decision-control model that predicts YES, NO, or TBD (to-be-determined) for high-stakes AI applications where uncertainty exists. The system learns deferral as an explicit outcome rather than hiding uncertainty in forced predictions, achieving 82.6% accuracy with auditable, policy-governed decision routing that can be inspected and controlled at inference time.