Key terms and where they are first defined or most thoroughly discussed.
A¶
ACS PUMS data: Chapters 1, 2, 3
Activation functions: Chapter 4
Agentic AI: Chapter 13
Agency (decision-making authority): Chapter 13
Algorithmic bias: Chapter 8
Attention mechanism: Chapter 11
Automation bias: Chapter 14
Autonomy dial: Chapter 13
B¶
Backpropagation: Chapter 4
Benchmark gaming: Chapter 14
BERT: Chapter 11
Bias, algorithmic: Chapter 8
Bias, statistical: Chapter 8
Blocking (record linkage): Chapter 5
Bounded agency: Chapters 13, 14
C¶
Calibration (fairness metric): Chapter 8
Census APIs: Chapters 1, 2
CIPSEA: Chapter 14
Classification: Chapter 1
Clustering: Chapter 6
Cohen’s kappa: Chapter 12
Compression Distortion (T3): Chapter 15
Compression Fidelity (CF): Chapter 15
Confidence-based routing: Chapter 12
Confidential models: Chapter 10
Confusion matrix: Chapter 1
Context window: Chapters 12, 15
Cross-validation: Chapter 2
Curse of dimensionality: Chapter 6
D¶
Decision trees: Chapter 3
Demographic parity: Chapter 8
Design effects: Chapter 5
Differential privacy: Chapter 9
Differential undercount: Chapter 8
Dimension reduction: Chapter 6
Disclosure risk: Chapter 9
E¶
Early stopping: Chapter 4
Embeddings: Chapter 11
Enforcement gap: Chapter 10
Entity resolution: Chapter 5
Epsilon (privacy parameter): Chapter 9
Equalized odds: Chapter 8
Evaluation rubric (10-dimension): Chapter 14
F¶
Fairness impossibility theorem: Chapter 8
Fairness metrics: Chapter 8
False State Injection (T2): Chapter 15
FCSM quality standards: Chapter 14
FedRAMP: Chapter 12
Feature importance: Chapters 2, 3
Feature importance stability: Chapter 3
Fine-tuning: Chapter 11
FSRDC: Chapter 9
G¶
Geographic codes (FIPS, GEOIDs): Chapter 1
Gini impurity: Chapter 3
Glossary: Glossary
GridSearchCV: Chapter 2
GroupKFold: Chapter 2
Graph structure: Chapter 5
H¶
Handoff documents: Chapter 15
Hot-deck imputation: Chapter 7
Hybrid human-LLM workflow: Chapter 12
Hyperparameter tuning: Chapter 2
I¶
Impact gap: Chapter 10
Imputation: Chapter 7
Intraclass correlation: Chapter 5
K¶
KFold: Chapter 2
K-means: Chapter 6
L¶
Laplace mechanism: Chapter 9
Large language models: Chapter 12
M¶
MCAR/MAR/MNAR: Chapter 7
Mean imputation: Chapter 7
Membership inference attacks: Chapter 10
MLP (multilayer perceptron): Chapter 4
Model cards: Chapters 8, 14
Model inversion attacks: Chapter 10
Model supply chain security: Chapter 11
Model version pinning: Chapter 12
Multi-head attention: Chapter 11
Multiple imputation: Chapter 7
N¶
NASS data lab: Chapter 10
Neural networks: Chapter 4
NIOCCS: Chapter 11
NIST AI 600-1: Chapter 14
NIST AI RMF: Chapter 14
O¶
Observe-decide-act-check loop: Chapter 13
OMB M-25-21: Chapter 14
Out-of-bag scoring: Chapter 3
Overfitting: Chapters 2, 3
P¶
Partial dependence plots: Chapter 4
PCA: Chapter 6
Permutation importance: Chapter 2
pMSE: Chapter 9
Positional encoding: Chapter 11
Predictive parity: Chapter 8
Privacy budget: Chapter 9
Privacy-utility tradeoff: Chapter 9
Probabilistic matching: Chapter 5
Prompt design: Chapter 12
Prompt versioning: Chapter 12
Prompt-as-agent: Chapter 13
R¶
Random forests: Chapter 3
Record linkage: Chapter 5
Regression: Chapter 1
Reproducibility checklist: Chapter 15
Rubin’s rules: Chapter 7
S¶
Scree plot: Chapter 6
SDL (Statistical Disclosure Limitation): Chapter 10
Seldon: Chapter 15
Semantic Drift (T1): Chapter 15
Sequential regression synthesis: Chapter 9
Session Continuity (SC): Chapter 15
SHAP values: Chapter 3
Silhouette score: Chapter 6
Six design principles (agentic AI): Chapter 13
SLMs (small language models): Chapter 11
Soundex: Chapter 5
Specification gap: Chapter 10
State Coherence (SCoh): Chapter 15
State Discontinuity (T5): Chapter 15
State Fidelity Validity (SFV): Chapter 15
State Provenance (SP): Chapter 15
State Supersession Failure (T4): Chapter 15
Stochastic regression imputation: Chapter 7
StratifiedKFold: Chapter 2
Stratification: Chapter 6
Subgroup accuracy decomposition: Chapter 8
Supervised learning: Chapter 1
Synthetic data: Chapter 9
T¶
Temperature (LLM): Chapter 12
Terminological Consistency (TC): Chapter 15
TEVV: Chapters 14, 15
Tokenization: Chapter 11
Tool (agentic AI): Chapter 13
Transitive closure: Chapter 5
Transformers: Chapter 11
t-SNE: Chapter 6
U¶
UMAP: Chapter 6
V¶
Validity types (classical): Chapter 15
W¶
Workflow (agentic AI): Chapter 13