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References: Risk-Based Recommendations and Knowledge Graphs

  1. Risk assessment - Wikipedia - Offers an accessible overview of Risk assessment, including definitions, methods, examples, limitations, and related concepts. This foundation helps students reason carefully about prioritizing risks and traceable recommendations in provenance-aware knowledge graphs.

  2. Knowledge graph - Wikipedia - Offers an accessible overview of Knowledge graph, including definitions, methods, examples, limitations, and related concepts. Its examples help students evaluate evidence for prioritizing risks and traceable recommendations in provenance-aware knowledge graphs.

  3. Graph database - Wikipedia - Offers an accessible overview of Graph database, including definitions, methods, examples, limitations, and related concepts. It supplies useful context for decisions about prioritizing risks and traceable recommendations in provenance-aware knowledge graphs.

  4. Knowledge Graphs: Data in Context for Responsive Businesses - Jesus Barrasa, Amy E. Hodler, and Jim Webber - O'Reilly Media - Introduces graph modeling, semantics, context, data integration, querying, and organizational applications through accessible business examples. Its sustained treatment supports work on prioritizing risks and traceable recommendations in provenance-aware knowledge graphs.

  5. Fundamentals of Risk Management (7th ed.) - Paul Hopkin - Kogan Page - Explains risk identification, likelihood, impact, controls, prioritization, governance, reporting, and decision-making across organizations. Its cases illuminate tradeoffs involved in prioritizing risks and traceable recommendations in provenance-aware knowledge graphs.

  6. AI Risk Management Framework - NIST - Provides the Govern, Map, Measure, and Manage functions for addressing validity, reliability, transparency, privacy, fairness, accountability, and other AI risks. Its methods give teams a starting point for prioritizing risks and traceable recommendations in provenance-aware knowledge graphs.

  7. Generative AI Profile - NIST - Extends the AI Risk Management Framework with generative-AI risks and suggested actions concerning confabulation, bias, privacy, information integrity, and human oversight. Its comparisons clarify choices involved in prioritizing risks and traceable recommendations in provenance-aware knowledge graphs.

  8. What Is a Knowledge Graph? - Neo4j - Introduces entities, relationships, semantics, provenance, querying, and graph structures for connecting organizational knowledge across otherwise separate sources. Its framework strengthens responsible work on prioritizing risks and traceable recommendations in provenance-aware knowledge graphs.

  9. OECD AI Principles - OECD - Presents human-centered principles for inclusive benefit, rights, fairness, transparency, robustness, safety, accountability, and responsible stewardship of artificial intelligence. Its examples show how evidence informs prioritizing risks and traceable recommendations in provenance-aware knowledge graphs.

  10. Demystifying Evals for AI Agents - Anthropic - Explains agent evaluation design, realistic tasks, outcome and process graders, repeated trials, transcript review, and analysis of variable behavior. Its implementation advice helps teams practice prioritizing risks and traceable recommendations in provenance-aware knowledge graphs.