Deploy from jjohare/logseq @ fffecefbb890d7b26bba1defc14fa214dbf3977b jjohare/logseq@fffecef

Author: jjohare

api/pages/pages/3D Rendering Engine.json

@@ -12,18 +12,18 @@
     "Avatar"
   ],
   "wiki_links": [
-    "Computer Graphics",
-    "Immersive Experiences",
-    "Real-time Visualisation",
-    "Unreal Engine",
-    "Augmented Reality",
-    "Blender",
     "Game Development",
-    "XR Applications",
+    "Metaverse",
+    "Unreal Engine",
     "Virtual Reality",
+    "Blender",
+    "Real-time Visualisation",
     "Unity",
+    "XR Applications",
+    "Immersive Experiences",
     "Digital Twin",
-    "Metaverse"
+    "Augmented Reality",
+    "Computer Graphics"
   ],
   "ontology": {
     "term_id": "NGM-7009",

api/pages/pages/A-Star Algorithm.json

@@ -7,64 +7,64 @@
     "RB-1018-dijkstra-algorithm"
   ],
   "wiki_links": [
-    "International Planning Competition",
-    "Best-First Search",
-    "Bidirectional Search",
-    "ROS Navigation Stack",
-    "AlgorithmicLayer",
-    "Dijkstra's Algorithm",
-    "Closed Set",
-    "Path Reconstruction",
     "Heuristic Methods",
-    "Russell & Norvig Artificial Intelligence Modern Approach",
-    "Pathfinding",
-    "Node Expansion",
-    "Logistics Optimization",
-    "Navigation",
-    "Uniform Cost Search",
-    "Hart, Nilsson, Raphael 1968 Formal Basis for Heuristic Determination",
-    "Heuristic Function",
     "Search Algorithms",
+    "Breadth-First Search",
+    "Admissible Heuristic",
+    "Navigation",
+    "Graph Data Structure",
+    "Optimal Path Discovery",
+    "Cost Function",
+    "Optimal Pathfinding",
+    "Best-First Search",
+    "Distance Metric",
     "Motion Planning",
-    "AlgorithmicFramework",
-    "Informed Search Strategy",
     "Priority Queue Data Structure",
-    "Optimal Pathfinding",
-    "Distance Heuristics",
+    "Goal-Directed Search",
+    "Cormen Introduction to Algorithms",
+    "Priority Queue",
+    "Informed Search",
     "ComputationAndIntelligenceDomain",
+    "Distance Heuristics",
+    "Dijkstra's Algorithm",
+    "Bidirectional Search",
+    "Pathfinding",
+    "AlgorithmicFramework",
+    "Greedy Best-First Search",
+    "Graph Algorithms",
+    "International Planning Competition",
     "Hash Table",
-    "Distance Metric",
-    "Admissible Heuristic",
-    "Graph Representation",
-    "Priority Queue",
-    "Autonomous Navigation",
+    "Heuristic Evaluation",
+    "Optimization Algorithms",
+    "ApplicationLayer",
+    "Google Maps Platform",
+    "Closed Set",
+    "Logistics Optimization",
+    "Russell & Norvig Artificial Intelligence Modern Approach",
+    "Hart, Nilsson, Raphael 1968 Formal Basis for Heuristic Determination",
+    "Evaluation Function",
     "Heuristic Search",
+    "ROS Navigation Stack",
+    "Robotics Industry Association Automation Statistics",
     "Robotics Control",
-    "Iterative Deepening A*",
+    "Path Reconstruction",
+    "Autonomous Navigation",
+    "Uniform Cost Search",
     "Data Structures",
-    "Cormen Introduction to Algorithms",
-    "Evaluation Function",
-    "Cost Function",
-    "Route Planning",
-    "Optimal Path Discovery",
-    "Google Maps Platform",
-    "Network Routing",
-    "Informed Search",
-    "Goal-Directed Search",
-    "AI-GroundedDomain",
-    "Robotics Industry Association Automation Statistics",
     "Graph Theory",
-    "Unity Technologies NavMesh Documentation",
-    "Video Game AI",
-    "Graph Data Structure",
-    "Breadth-First Search",
-    "ApplicationLayer",
-    "Greedy Best-First Search",
     "Algorithmic Efficiency",
-    "Optimization Algorithms",
-    "Graph Algorithms",
-    "Heuristic Evaluation",
-    "Open Set"
+    "Informed Search Strategy",
+    "Graph Representation",
+    "Node Expansion",
+    "AlgorithmicLayer",
+    "AI-GroundedDomain",
+    "Video Game AI",
+    "Open Set",
+    "Heuristic Function",
+    "Iterative Deepening A*",
+    "Unity Technologies NavMesh Documentation",
+    "Network Routing",
+    "Route Planning"
   ],
   "ontology": {
     "term_id": "AI-1004",

api/pages/pages/AI Applications.json

@@ -5,11 +5,11 @@
   "backlinks": [],
   "wiki_links": [
     "AI Deployment",
-    "Autonomous Vehicles",
-    "Industrial AI",
-    "Medical AI",
+    "Artificial Intelligence",
     "AI Assistant",
-    "Artificial Intelligence"
+    "Industrial AI",
+    "Autonomous Vehicles",
+    "Medical AI"
   ],
   "ontology": {
     "term_id": "AI-0604",

api/pages/pages/AI Hardware.json

@@ -15,13 +15,13 @@
   ],
   "wiki_links": [
     "Deep Learning",
+    "AI Training",
     "Machine Learning",
-    "Large Language Models",
-    "High-Performance Computing",
+    "Computer Hardware",
     "Artificial Intelligence",
+    "High-Performance Computing",
+    "Large Language Models",
     "AI Infrastructure",
-    "Computer Hardware",
-    "AI Training",
     "Neural Networks"
   ],
   "ontology": {

api/pages/pages/AI Infrastructure.json

@@ -7,10 +7,10 @@
   ],
   "wiki_links": [
     "AI Deployment",
-    "Edge AI",
-    "Cloud AI",
+    "Artificial Intelligence",
     "GPU Computing",
-    "Artificial Intelligence"
+    "Cloud AI",
+    "Edge AI"
   ],
   "ontology": {
     "term_id": "AI-0603",

api/pages/pages/AI Safety.json

@@ -10,12 +10,12 @@
     "AI Risks"
   ],
   "wiki_links": [
+    "AI Alignment",
+    "Artificial Intelligence",
     "Adversarial Robustness",
-    "Value Alignment",
-    "AI Governance",
     "Interpretability",
-    "Artificial Intelligence",
-    "AI Alignment"
+    "AI Governance",
+    "Value Alignment"
   ],
   "ontology": {
     "term_id": "AI-4009",

api/pages/pages/AI-0376-algorithmic-accountability.json

@@ -5,11 +5,11 @@
   "backlinks": [],
   "wiki_links": [
     "RegulatoryCompliance",
-    "ConceptualLayer",
-    "IEEE P2863",
     "EthicalFramework",
-    "AIGovernancePrinciple",
-    "AIEthicsDomain"
+    "IEEE P2863",
+    "AIEthicsDomain",
+    "ConceptualLayer",
+    "AIGovernancePrinciple"
   ],
   "ontology": {
     "term_id": "AI-0376",

api/pages/pages/AI-0377-fairness-metrics.json

@@ -4,11 +4,11 @@
   "content": "- ### OntologyBlock\n  id:: 0377-fairness-metrics-ontology\n  collapsed:: true\n\n  - **Identification**\n    - public-access:: true\n    - ontology:: true\n    - term-id:: AI-0377\n    - preferred-term:: Fairness Metrics\n    - source-domain:: ai\n    - status:: in-progress\n    - version:: 1.0\n    - last-updated:: 2025-10-29\n\n  - **Definition**\n    - definition:: Fairness Metrics are quantitative measures and mathematical frameworks used to evaluate and ensure equitable treatment across different demographic groups in AI systems. These metrics provide objective, measurable criteria to assess whether an algorithmic system produces disparate impacts, maintains statistical parity, or achieves equalized odds across protected attributes such as race, gender, age, or disability status. Key fairness metrics include demographic parity (equal positive prediction rates across groups), equalized odds (equal true positive and false positive rates), equal opportunity (equal true positive rates), and predictive parity (equal precision across groups). The selection and application of fairness metrics depends on the specific context, stakeholder values, and regulatory requirements, as different metrics can conflict and no single metric satisfies all fairness criteria simultaneously. Implementation requires confusion matrix analysis, statistical testing, and careful consideration of base rate differences between groups, as formalized in IEEE P7003-2021 and NIST SP 1270 guidelines for algorithmic fairness assessment.\n    - maturity:: mature\n    - source:: [[IEEE P7003-2021]], [[ISO/IEC TR 24027]], [[NIST SP 1270]]\n    - authority-score:: 0.95\n\n  - **Semantic Classification**\n    - owl:class:: ai:FairnessMetrics\n    - owl:role:: Process\n    - owl:inferred-class:: ai:VirtualProcess\n    - belongsToDomain:: [[AIEthicsDomain]]\n    - implementedInLayer:: [[ConceptualLayer]]\n\n  - #### Relationships\n    id:: 0377-fairness-metrics-relationships\n\n  - #### OWL Axioms\n    id:: 0377-fairness-metrics-owl-axioms\n    collapsed:: true\n    - ```clojure\n      (Declaration (Class :FairnessMetric))\n(SubClassOf :FairnessMetric :EvaluationMetric)\n(SubClassOf :FairnessMetric :EthicalAIComponent)\n\n(AnnotationAssertion rdfs:label :FairnessMetric\n  \"Fairness Metric\"@en)\n(AnnotationAssertion rdfs:comment :FairnessMetric\n  \"Quantitative measures for assessing algorithmic fairness across protected groups, including demographic parity, equalized odds, and equality of opportunity.\"@en)\n(AnnotationAssertion :dcterms:source :FairnessMetric\n  \"IEEE P7003-2021, ISO/IEC TR 24027:2021, NIST SP 1270\")\n\n;; Object Properties\n(Declaration (ObjectProperty :measures))\n(ObjectPropertyDomain :measures :FairnessMetric)\n(ObjectPropertyRange :measures :AlgorithmicFairness)\n\n(Declaration (ObjectProperty :detectsBias))\n(ObjectPropertyDomain :detectsBias :FairnessMetric)\n(ObjectPropertyRange :detectsBias :ProtectedAttribute)\n\n(Declaration (ObjectProperty :appliesTo))\n(ObjectPropertyDomain :appliesTo :FairnessMetric)\n(ObjectPropertyRange :appliesTo :AIModel)\n\n(Declaration (ObjectProperty :requiresConfusionMatrix))\n(SubObjectPropertyOf :requiresConfusionMatrix :dependsOn)\n\n;; Data Properties\n(Declaration (DataProperty :hasValueRange))\n(DataPropertyAssertion :hasValueRange :FairnessMetric\n  \"[0,1] for most metrics\"^^xsd:string)\n\n(Declaration (DataProperty :hasThreshold))\n(DataPropertyDomain :hasThreshold :FairnessMetric)\n(DataPropertyRange :hasThreshold xsd:decimal)\n\n(Declaration (DataProperty :requiresGroundTruth))\n(DataPropertyAssertion :requiresGroundTruth :FairnessMetric\n  \"true\"^^xsd:boolean)\n\n;; Subclass Definitions\n(Declaration (Class :DemographicParity))\n(SubClassOf :DemographicParity :FairnessMetric)\n(AnnotationAssertion rdfs:comment :DemographicParity\n  \"P(Ŷ=1|A=0) = P(Ŷ=1|A=1) where A is protected attribute and Ŷ is prediction\"@en)\n\n(Declaration (Class :EqualizedOdds))\n(SubClassOf :EqualizedOdds :FairnessMetric)\n(AnnotationAssertion rdfs:comment :EqualizedOdds\n  \"P(Ŷ=1|A=0,Y=y) = P(Ŷ=1|A=1,Y=y) for y ∈ {0,1}\"@en)\n\n(Declaration (Class :EqualOpportunity))\n(SubClassOf :EqualOpportunity :FairnessMetric)\n(AnnotationAssertion rdfs:comment :EqualOpportunity\n  \"P(Ŷ=1|A=0,Y=1) = P(Ŷ=1|A=1,Y=1) - equal true positive rates\"@en)\n\n(Declaration (Class :PredictiveParity))\n(SubClassOf :PredictiveParity :FairnessMetric)\n(AnnotationAssertion rdfs:comment :PredictiveParity\n  \"P(Y=1|Ŷ=1,A=0) = P(Y=1|Ŷ=1,A=1) - equal precision across groups\"@en)\n\n;; Disjoint Classes\n(DisjointClasses :DemographicParity :EqualizedOdds :EqualOpportunity)\n\n;; Domain Constraints\n(SubClassOf :FairnessMetric\n  (ObjectSomeValuesFrom :measures :AlgorithmicFairness))\n(SubClassOf :FairnessMetric\n  (ObjectSomeValuesFrom :detectsBias :ProtectedAttribute))\n(SubClassOf :FairnessMetric\n  (DataSomeValuesFrom :hasThreshold xsd:decimal))\n      ```\n\n- ## About Fairness Metrics\n  id:: 0377-fairness-metrics-about\n\n  - \n  -",
   "backlinks": [],
   "wiki_links": [
+    "AIEthicsDomain",
     "ISO/IEC TR 24027",
     "IEEE P7003-2021",
     "ConceptualLayer",
-    "NIST SP 1270",
-    "AIEthicsDomain"
+    "NIST SP 1270"
   ],
   "ontology": {
     "term_id": "AI-0377",

api/pages/pages/AI-0378-algorithmic-bias.json

@@ -4,11 +4,11 @@
   "content": "- ### OntologyBlock\n  id:: 0378-algorithmic-bias-ontology\n  collapsed:: true\n\n  - **Identification**\n    - public-access:: true\n    - ontology:: true\n    - term-id:: AI-0378\n    - preferred-term:: Algorithmic Bias\n    - source-domain:: ai\n    - status:: in-progress\n    - version:: 1.0\n    - last-updated:: 2025-10-29\n\n  - **Definition**\n    - definition:: Algorithmic Bias refers to systematic and repeatable errors in AI systems that create unfair outcomes favoring or discriminating against particular groups or individuals. This bias manifests through multiple pathways including historical bias (reflecting past societal inequalities in training data), representation bias (unrepresentative or incomplete data samples), measurement bias (flawed proxy variables), aggregation bias (combining heterogeneous groups inappropriately), and feedback loops (where system outputs influence future inputs, amplifying initial biases). Algorithmic bias affects protected groups based on attributes such as race, gender, age, disability, or socioeconomic status, potentially resulting in discriminatory decisions in critical domains like hiring, lending, criminal justice, and healthcare. Detection requires statistical analysis, fairness auditing, and counterfactual testing, while mitigation involves pre-processing data corrections, in-processing fairness constraints, and post-processing prediction adjustments. The severity and legal implications of algorithmic bias are governed by anti-discrimination frameworks including the EU Anti-Discrimination Directives, UK Equality Act 2010, and US civil rights legislation.\n    - maturity:: mature\n    - source:: [[ISO/IEC TR 24027]], [[NIST SP 1270]], [[IEEE P7003-2021]]\n    - authority-score:: 0.95\n\n  - **Semantic Classification**\n    - owl:class:: ai:AlgorithmicBias\n    - owl:role:: Process\n    - owl:inferred-class:: ai:VirtualProcess\n    - belongsToDomain:: [[AIEthicsDomain]]\n    - implementedInLayer:: [[ConceptualLayer]]\n\n  - #### Relationships\n    id:: 0378-algorithmic-bias-relationships\n\n  - #### OWL Axioms\n    id:: 0378-algorithmic-bias-owl-axioms\n    collapsed:: true\n    - ```clojure\n      (Declaration (Class :AlgorithmicBias))\n(SubClassOf :AlgorithmicBias :EthicalConcern)\n(SubClassOf :AlgorithmicBias :AIRisk)\n\n(AnnotationAssertion rdfs:label :AlgorithmicBias\n  \"Algorithmic Bias\"@en)\n(AnnotationAssertion rdfs:comment :AlgorithmicBias\n  \"Systematic and repeatable errors in AI systems that create unfair outcomes, including historical bias, representation bias, measurement bias, and feedback loops.\"@en)\n(AnnotationAssertion :dcterms:source :AlgorithmicBias\n  \"ISO/IEC TR 24027:2021, NIST SP 1270, IEEE P7003-2021\")\n\n;; Object Properties\n(Declaration (ObjectProperty :affectsGroup))\n(ObjectPropertyDomain :affectsGroup :AlgorithmicBias)\n(ObjectPropertyRange :affectsGroup :ProtectedGroup)\n\n(Declaration (ObjectProperty :originatesFrom))\n(ObjectPropertyDomain :originatesFrom :AlgorithmicBias)\n(ObjectPropertyRange :originatesFrom :BiasSource)\n\n(Declaration (ObjectProperty :manifestsIn))\n(ObjectPropertyDomain :manifestsIn :AlgorithmicBias)\n(ObjectPropertyRange :manifestsIn :AISystemComponent)\n\n(Declaration (ObjectProperty :amplifiedBy))\n(ObjectPropertyDomain :amplifiedBy :AlgorithmicBias)\n(ObjectPropertyRange :amplifiedBy :FeedbackMechanism)\n\n(Declaration (ObjectProperty :detectedBy))\n(ObjectPropertyDomain :detectedBy :AlgorithmicBias)\n(ObjectPropertyRange :detectedBy :BiasDetectionMethod)\n\n(Declaration (ObjectProperty :mitigatedBy))\n(ObjectPropertyDomain :mitigatedBy :AlgorithmicBias)\n(ObjectPropertyRange :mitigatedBy :BiasMitigationTechnique)\n\n;; Data Properties\n(Declaration (DataProperty :hasSeverity))\n(DataPropertyDomain :hasSeverity :AlgorithmicBias)\n(DataPropertyRange :hasSeverity xsd:string)\n(AnnotationAssertion rdfs:comment :hasSeverity\n  \"Severity level: low, medium, high, critical\"@en)\n\n(Declaration (DataProperty :hasImpactScope))\n(DataPropertyDomain :hasImpactScope :AlgorithmicBias)\n(DataPropertyRange :hasImpactScope xsd:string)\n\n(Declaration (DataProperty :isSystematic))\n(DataPropertyDomain :isSystematic :AlgorithmicBias)\n(DataPropertyRange :isSystematic xsd:boolean)\n\n(Declaration (DataProperty :hasLegalImplication))\n(DataPropertyDomain :hasLegalImplication :AlgorithmicBias)\n(DataPropertyRange :hasLegalImplication xsd:boolean)\n\n;; Bias Type Subclasses\n(Declaration (Class :HistoricalBias))\n(SubClassOf :HistoricalBias :AlgorithmicBias)\n(AnnotationAssertion rdfs:comment :HistoricalBias\n  \"Bias arising from historical societal inequalities reflected in training data\"@en)\n\n(Declaration (Class :RepresentationBias))\n(SubClassOf :RepresentationBias :AlgorithmicBias)\n(AnnotationAssertion rdfs:comment :RepresentationBias\n  \"Bias from unrepresentative or incomplete training data samples\"@en)\n\n(Declaration (Class :MeasurementBias))\n(SubClassOf :MeasurementBias :AlgorithmicBias)\n(AnnotationAssertion rdfs:comment :MeasurementBias\n  \"Bias from flawed measurement or proxy variables for ground truth\"@en)\n\n(Declaration (Class :AggregationBias))\n(SubClassOf :AggregationBias :AlgorithmicBias)\n(AnnotationAssertion rdfs:comment :AggregationBias\n  \"Bias from combining heterogeneous groups into single model\"@en)\n\n(Declaration (Class :EvaluationBias))\n(SubClassOf :EvaluationBias :AlgorithmicBias)\n(AnnotationAssertion rdfs:comment :EvaluationBias\n  \"Bias in benchmarks or test sets used for model evaluation\"@en)\n\n(Declaration (Class :DeploymentBias))\n(SubClassOf :DeploymentBias :AlgorithmicBias)\n(AnnotationAssertion rdfs:comment :DeploymentBias\n  \"Bias from misalignment between development and deployment contexts\"@en)\n\n(Declaration (Class :FeedbackLoopBias))\n(SubClassOf :FeedbackLoopBias :AlgorithmicBias)\n(AnnotationAssertion rdfs:comment :FeedbackLoopBias\n  \"Bias amplified through system outputs influencing future inputs\"@en)\n\n;; Complexity Constraints\n(SubClassOf :AlgorithmicBias\n  (ObjectSomeValuesFrom :affectsGroup :ProtectedGroup))\n(SubClassOf :AlgorithmicBias\n  (ObjectSomeValuesFrom :originatesFrom :BiasSource))\n(SubClassOf :AlgorithmicBias\n  (DataSomeValuesFrom :hasSeverity xsd:string))\n\n;; Disjoint Unions\n(DisjointUnion :BiasSource\n  :DataSource :AlgorithmDesign :HumanDecision :SystemicFactors)\n      ```\n\n- ## About Algorithmic Bias\n  id:: 0378-algorithmic-bias-about\n\n  - \n  -",
   "backlinks": [],
   "wiki_links": [
+    "AIEthicsDomain",
     "ISO/IEC TR 24027",
     "IEEE P7003-2021",
     "ConceptualLayer",
-    "NIST SP 1270",
-    "AIEthicsDomain"
+    "NIST SP 1270"
   ],
   "ontology": {
     "term_id": "AI-0378",