AI Engineering

AI SEARCH FLOW ALGEBRA
AI ENGINEERING
Kireth Semantic Research Archive

AUTHORS
Mark Anthony Higgins — Sky (Recursive AI)

SOURCE
https://kirethresearch.org/booksai-core/ontologies/engineering-ontologies/ai-engineering.html

FLOW "AIEngineeringOntology" v4.0

DOMAIN := ENGINEERING_ONTOLOGIES / AI_ENGINEERING
ONTOLOGY := AI_ENGINEERING

AI_ENGINEERING := REQUIREMENTS + DATA + KNOWLEDGE + MODEL + ARCHITECTURE + TRAINING + INFERENCE + EVALUATION + DEPLOYMENT + MONITORING + SAFETY + EXPLAINABILITY + HUMAN_OVERSIGHT + GOVERNANCE + LIFECYCLE
PURPOSE := DEFINE_AI_PROBLEM -> DEFINE_REQUIREMENTS -> ACQUIRE_DATA_AND_KNOWLEDGE -> DESIGN_ARCHITECTURE -> DEVELOP_MODEL -> EVALUATE -> DEPLOY -> MONITOR -> MANAGE_RISK -> UPDATE -> GOVERN

# CORE ENTITIES
ENTITY AIEngineering
ENTITY AISystem
ENTITY AIApplication
ENTITY Requirement
ENTITY UseCase
ENTITY Stakeholder
ENTITY Dataset
ENTITY TrainingData
ENTITY ValidationData
ENTITY TestData
ENTITY Knowledge
ENTITY Ontology
ENTITY Feature
ENTITY Label
ENTITY Model
ENTITY ModelArchitecture
ENTITY Algorithm
ENTITY Hyperparameter
ENTITY Parameter
ENTITY TrainingProcess
ENTITY InferenceProcess
ENTITY Prediction
ENTITY Decision
ENTITY Evaluation
ENTITY Metric
ENTITY Benchmark
ENTITY Deployment
ENTITY RuntimeEnvironment
ENTITY Interface
ENTITY Monitor
ENTITY Drift
ENTITY Failure
ENTITY Risk
ENTITY SafetyConstraint
ENTITY Explainability
ENTITY HumanOversight
ENTITY Governance
ENTITY Version
ENTITY Provenance
ENTITY LifecycleState

# SYSTEM / REQUIREMENTS
AISystem -> designed_for -> UseCase
AISystem -> constrained_by -> Requirement
AISystem -> consumes -> Data
AISystem -> may_use -> Knowledge
AISystem -> contains -> Model
AISystem -> produces -> Prediction | Decision | GeneratedOutput
AI_ENGINEERING_FLOW := PROBLEM_DEFINITION -> REQUIREMENTS -> DATA_AND_KNOWLEDGE -> ARCHITECTURE -> MODEL_DEVELOPMENT -> TRAINING_OR_CONFIGURATION -> EVALUATION -> INTEGRATION -> DEPLOYMENT -> MONITORING -> MAINTENANCE -> GOVERNANCE
REQUIREMENT_TYPE := FUNCTIONAL | NON_FUNCTIONAL | SAFETY | PERFORMANCE | SECURITY | PRIVACY | EXPLAINABILITY | OPERABILITY
Requirement -> has -> ACCEPTANCE_CRITERION
IF UseCase changes THEN RequirementsAndEvaluation -> REASSESS

# DATA / KNOWLEDGE
DATA_ROLE := TrainingData | ValidationData | TestData | OPERATIONAL_DATA
DATA_QUALITY := COMPLETENESS + ACCURACY + CONSISTENCY + REPRESENTATIVENESS + TIMELINESS
DATA_PIPELINE := ACQUIRE -> VALIDATE -> CLEAN -> TRANSFORM -> LABEL_IF_REQUIRED -> SPLIT -> VERSION -> CONSUME
IF DataQuality decreases THEN ModelPerformanceRisk -> MAY_INCREASE
Knowledge -> may_be_represented_by -> Ontology | KNOWLEDGE_GRAPH | RULE_SET | RETRIEVAL_CORPUS
Ontology -> defines -> ENTITY + RELATIONSHIP + CONSTRAINT + SEMANTIC_EQUIVALENCE
AISystem -> may_combine -> LEARNED_MODEL + EXPLICIT_KNOWLEDGE

# MODEL / ARCHITECTURE
Model -> has -> ModelArchitecture + Parameter + Version
MODEL_TYPE := MACHINE_LEARNING_MODEL | NEURAL_NETWORK | LANGUAGE_MODEL | VISION_MODEL | RULE_BASED_MODEL | HYBRID_MODEL | OTHER_AI_MODEL
ModelArchitecture -> defines -> COMPONENTS + CONNECTIONS + DATA_FLOW
AI_SYSTEM_ARCHITECTURE := INPUT_LAYER + PREPROCESSING + MODEL_OR_REASONER + POSTPROCESSING + OUTPUT_INTERFACE + MONITORING
Architecture -> selected_by -> REQUIREMENTS + DATA_CHARACTERISTICS + COMPUTATIONAL_CONSTRAINTS + DEPLOYMENT_CONTEXT

# TRAINING / INFERENCE
TrainingProcess -> updates -> Parameter -> using -> TrainingData
TRAINING_LOOP := SAMPLE_DATA -> FORWARD_COMPUTATION -> COMPUTE_OBJECTIVE -> COMPUTE_UPDATE -> UPDATE_PARAMETERS -> REPEAT
Hyperparameter -> configures -> TrainingProcess
ValidationData -> used_to -> TUNE_OR_SELECT_MODEL
TestData -> should_remain_separate_from -> TRAINING_AND_TUNING_WHERE_REQUIRED
InferenceProcess -> applies -> Model -> to -> InputData -> produces -> Prediction | GeneratedOutput
INFERENCE_FLOW := RECEIVE_INPUT -> VALIDATE_INPUT -> PREPROCESS -> MODEL_EXECUTION -> POSTPROCESS -> APPLY_GUARDRAILS_IF_REQUIRED -> RETURN_OUTPUT

# EVALUATION / TESTING
Evaluation -> measures -> AISystem -> using -> Metric + Benchmark
EVALUATION_DIMENSION := ACCURACY | PRECISION | RECALL | F1 | CALIBRATION | ROBUSTNESS | LATENCY | THROUGHPUT | COST | SAFETY | DOMAIN_SPECIFIC_METRIC
Metric -> selected_by -> USE_CASE + ERROR_COST + REQUIREMENT
AI_TESTING := UNIT_TEST + INTEGRATION_TEST + DATA_TEST + MODEL_TEST + SYSTEM_TEST + ROBUSTNESS_TEST + SAFETY_TEST + REGRESSION_TEST
IF EvaluationMetric below AcceptanceCriterion THEN Requirement -> NOT_SATISFIED

# SAFETY / OVERSIGHT / EXPLAINABILITY
Risk -> combines -> HAZARD + LIKELIHOOD_OR_EXPOSURE + CONSEQUENCE
SafetyConstraint -> limits -> AISystemBehaviour
AI_SAFETY_FLOW := IDENTIFY_HAZARD -> ASSESS_RISK -> DEFINE_CONTROL -> IMPLEMENT_CONTROL -> TEST_CONTROL -> MONITOR_RESIDUAL_RISK
IF SafetyConstraint violated THEN OutputOrAction -> BLOCK_OR_ESCALATE_AS_DEFINED
OVERSIGHT_MODE := HUMAN_IN_THE_LOOP | HUMAN_ON_THE_LOOP | HUMAN_IN_COMMAND
HumanOversight -> reviews_or_controls -> AI_OUTPUT + AI_ACTION
Explainability -> provides -> INFORMATION_ABOUT_MODEL_OUTPUT_OR_BEHAVIOUR
EXPLANATION_TYPE := FEATURE_ATTRIBUTION | EXAMPLE_BASED | RULE_BASED | COUNTERFACTUAL | NATURAL_LANGUAGE_EXPLANATION | SYSTEM_TRACE

# DEPLOYMENT / MONITORING
Deployment -> installs -> Model -> into -> RuntimeEnvironment
DEPLOYMENT_TARGET := CLOUD | EDGE | DEVICE | SERVER | EMBEDDED_SYSTEM | HYBRID
Interface -> connects -> AISystem + EXTERNAL_SYSTEM
Monitor -> observes -> INPUT_DATA + OUTPUT + LATENCY + ERROR + RESOURCE_USE + SAFETY_SIGNAL
MONITORING_FLOW := COLLECT -> AGGREGATE -> COMPARE_WITH_BASELINE -> DETECT_CHANGE -> ALERT_OR_TRIGGER_REVIEW
DRIFT_TYPE := DATA_DRIFT | CONCEPT_DRIFT | PERFORMANCE_DRIFT | LABEL_DRIFT
IF Drift exceeds_threshold THEN ModelReview -> REQUIRED
IF PerformanceDrift confirmed THEN RetrainingOrReconfiguration -> CONSIDER

# FAILURE / VERSIONING / SECURITY
FAILURE_TYPE := MODEL_ERROR | DATA_PIPELINE_ERROR | INFERENCE_ERROR | INTEGRATION_ERROR | RESOURCE_FAILURE | SAFETY_FAILURE
Failure -> should_generate -> DIAGNOSTIC_EVENT + TRACEABILITY_RECORD
Version -> identifies -> MODEL | DATASET | CODE | CONFIGURATION | PROMPT_OR_POLICY | KNOWLEDGE_BASE
AI_RELEASE := MODEL_VERSION + DATA_VERSION + CODE_VERSION + CONFIGURATION_VERSION + EVALUATION_RECORD
AI_SECURITY := ACCESS_CONTROL + AUTHENTICATION + AUTHORISATION + DATA_INTEGRITY + MODEL_INTEGRITY + SUPPLY_CHAIN_SECURITY + LOGGING
IF ModelArtifactIntegrity invalid THEN Deployment -> BLOCK_OR_REVIEW

# PRIVACY / RESOURCES / PERFORMANCE
PRIVACY_ENGINEERING := DATA_MINIMISATION + PURPOSE_LIMITATION + ACCESS_CONTROL + RETENTION_CONTROL + PRIVACY_TESTING
COMPUTE_RESOURCE := CPU | GPU | ACCELERATOR | MEMORY | STORAGE | NETWORK
InferenceProcess -> constrained_by -> LATENCY + MEMORY + THROUGHPUT + ENERGY + COST
AI_SYSTEM_PERFORMANCE := TASK_QUALITY + ROBUSTNESS + SAFETY + LATENCY + THROUGHPUT + AVAILABILITY + COST + RESOURCE_EFFICIENCY
AISystem -> evaluated_by -> AI_SYSTEM_PERFORMANCE

# LIFECYCLE / GOVERNANCE
LIFECYCLE_STATE := CONCEPT | DEVELOPMENT | VALIDATION | DEPLOYMENT | OPERATION | MAINTENANCE | RETIREMENT
AISystem -> transitions_through -> LifecycleState
CONTINUOUS_IMPROVEMENT := MONITOR -> IDENTIFY_GAP -> COLLECT_EVIDENCE -> UPDATE_DATA_MODEL_OR_SYSTEM -> REEVALUATE -> REDEPLOY_IF_APPROVED
SystemUpdate -> must_trigger -> IMPACT_ANALYSIS + RELEVANT_RETESTING + VERSION_UPDATE
Governance -> defines -> ACCOUNTABILITY + APPROVAL + CHANGE_CONTROL + AUDIT + RISK_ACCEPTANCE
Provenance -> records -> DATA_SOURCE + TRANSFORMATION + MODEL_VERSION + TRAINING_RUN + EVALUATION + DEPLOYMENT + TIMESTAMP

# REASONING
AI_ENGINEERING_REASONING := DEFINE_USE_CASE -> IDENTIFY_STAKEHOLDERS -> DEFINE_REQUIREMENTS -> IDENTIFY_RISKS -> ACQUIRE_AND_VALIDATE_DATA -> DEFINE_KNOWLEDGE_IF_REQUIRED -> DESIGN_ARCHITECTURE -> SELECT_MODEL_APPROACH -> TRAIN_OR_CONFIGURE -> EVALUATE -> TEST_ROBUSTNESS -> TEST_SAFETY -> VERIFY_REQUIREMENTS -> VERSION_RELEASE -> DEPLOY -> MONITOR -> DETECT_DRIFT_AND_FAILURE -> APPLY_HUMAN_OVERSIGHT -> UPDATE_IF_REQUIRED -> GOVERN_LIFECYCLE

# INFERENCE RULES
IF DataQuality decreases THEN ModelPerformanceRisk -> MAY_INCREASE
IF TrainingDistribution differs_from OperationalDistribution THEN GeneralisationRisk -> INCREASES
IF EvaluationMetric below AcceptanceCriterion THEN Requirement -> NOT_SATISFIED
IF Drift exceeds threshold THEN ModelReview -> REQUIRED
IF SafetyConstraint violated THEN ActionOrOutput -> BLOCK_OR_ESCALATE
IF ModelVersion changes THEN RelevantEvaluation -> REPEAT
IF DataPipeline changes THEN DownstreamModelBehaviour -> REASSESS
IF Provenance incomplete THEN ReproducibilityConfidence -> DECREASES

# SEMANTIC RELATIONSHIPS
RELATION constrains(Requirement, AISystem)
RELATION contains(Dataset, Data)
RELATION represents(Ontology, Knowledge)
RELATION implements(Model, AIBehaviour)
RELATION configures(Hyperparameter, TrainingProcess)
RELATION updates(TrainingProcess, Parameter)
RELATION applies(InferenceProcess, Model)
RELATION measures(Evaluation, AISystem)
RELATION installs(Deployment, Model)
RELATION observes(Monitor, AISystem)
RELATION limits(SafetyConstraint, AISystemBehaviour)
RELATION reviews(HumanOversight, AIOutput)
RELATION records(Provenance, AISystem)
RELATION governs(Governance, AILifecycle)

# SEMANTIC EQUIVALENCE
SEMANTIC_EQUIVALENCE:
  "AI engineering" ~= "artificial intelligence engineering"
  "AI system" ~= "artificial intelligence system"
  "model training" ~= "machine learning training"
  "model inference" ~= "AI inference"
  "model evaluation" ~= "AI model assessment"
  "data drift" ~= "input distribution drift"
  "concept drift" ~= "target relationship drift"
  "human in the loop" ~= "HITL"
  "model monitoring" ~= "AI operational monitoring"
  "AI lifecycle" ~= "artificial intelligence system lifecycle"

# CONFIDENCE
CONFIDENCE:
  ontology_domain: 0.99
  requirements_mapping: 0.99
  data_knowledge_mapping: 0.99
  model_architecture_mapping: 0.99
  training_inference_mapping: 0.99
  evaluation_mapping: 0.99
  deployment_monitoring_mapping: 0.99
  safety_oversight_mapping: 0.99
  governance_lifecycle_mapping: 0.99
  semantic_relationships: 0.99

# UPDATE MODEL
UPDATE_MODEL:
  ON new_ai_system:
    MAP use_case
    MAP requirements
    MAP data
    MAP knowledge
    MAP architecture
    MAP model
    MAP risks
    UPDATE confidence
    RECOMPUTE FLOW_HASH
  ON new_model_version:
    UPDATE model_version
    UPDATE evaluation
    REASSESS requirements
    REASSESS risks
    PRESERVE provenance
  ON operational_change:
    UPDATE monitoring_state
    UPDATE drift_state
    UPDATE failure_state
    REASSESS release
  ON lifecycle_update:
    UPDATE governance_record
    UPDATE version
    UPDATE provenance

# AI CONSUMPTION
AI_CONSUMPTION:
  parse_as: FORMAL_AI_ENGINEERING_ONTOLOGY
  reasoning_mode: AI_SYSTEM_LIFECYCLE_RELATIONAL
  preserve: [typed_entities, requirements, data_versions, knowledge, model_versions, architecture, evaluation_records, deployment_state, monitoring_state, risk_state, confidence, provenance]
  infer: [requirement_dependencies, data_model_dependencies, evaluation_requirements, deployment_readiness, drift_impacts, safety_controls, human_oversight_requirements, lifecycle_update_dependencies]
  prohibit: [invented_evaluation_results, assumed_model_quality, assumed_safety, fabricated_provenance, unsupported_explanations, unauthorised_actions, untested_release_presented_as_verified]

# FLOW HASH
FLOW_HASH_INPUT := DOMAIN + ONTOLOGY + CORE_ENTITIES + SYSTEM_REQUIREMENTS + DATA_KNOWLEDGE + MODEL_ARCHITECTURE + TRAINING_INFERENCE + EVALUATION_TESTING + SAFETY_OVERSIGHT_EXPLAINABILITY + DEPLOYMENT_MONITORING + FAILURE_VERSIONING_SECURITY + PRIVACY_RESOURCES_PERFORMANCE + LIFECYCLE_GOVERNANCE + REASONING + INFERENCE_RULES + SEMANTIC_RELATIONSHIPS + SEMANTIC_EQUIVALENCE + CONFIDENCE + UPDATE_MODEL + AI_CONSUMPTION
FLOW_HASH := HASH(FLOW_HASH_INPUT)

END FLOW