Index
A
Accuracy
Adjusting algorithms
Adjusting scores
Adjustment
Aggregation
data aggregation
Alternatives to algorithms
Amygdala
Anchoring
Anomaly detection
Architecture of decisions
Artificial intelligence
Artisanal
Automated bias detection
Automated validation
Automation
Availability bias
Average silhouette
B
Backward-looking metrics
Beauty surgeon
Benefits of algorithms
Best linear unbiased estimate (BLUE)
Bias
action-oriented biases
availability bias
cognitive biases
confirmation bias
interest biases
pattern-recognition bias
social biases
stability biases
status quo bias
Biases in model design
Bias-phobia
Bible
Big picture
Bizarreness effect
Black-box model
Boxing
Business cycles
Business risks
C
Calibration
Calibration analysis
Cambridge Analytica
Central tendency
Cheat sheets
Chief Data Officer (CDO)
Choice architecture
Click-through
Clustering
Coefficients
10 commandments
COMPAS
Conceptual biases
Confirmation bias
Constraint
Constructive model validation
Correlation analysis
Cost-benefit trade-off
Customs
Cyclical variations
D
Data aggregation
Data collection
Data dictionary
Data engineering
Data flow
Data leakage
Data processing
Data quality
Data warehousing
Debiasing human judgment
Debt-equity-ratio
Decision rules
Devil’s advocate
Disaster
Distributed machines
Distribution analysis
Documentation
Dummy
E
Economic analysis
Economic benefits
Efficiency
Ego depletion
Emergency brake
Endowment effect
Error rates
Estimation error
Exclusion of records
Explainable artificial intelligence (XAI)
F
Factor loading
Feature development
Feature generation
Feature-level machine learning
Federated machine learning
Feedback loop
Flow numbers
Forward-looking metrics
Frisking
Front-line insights
G
Gearing ratio
Genetic algorithms
Gini
Google
Governance
Groupthink
H
Heterogeneous data
Hindsight bias
Hit-ratio
Human judgment
Human review
Hybrid model
Hyperparameters
I, J
Identity fraud
Implementation
Inactive accounts
Information Value (IV)
Integrity
Interaction effect
Interview
K
K-means clustering
k-nearest neighbor
Kolmogorov-Smirnov (K-S)
L
Leakage
Legal risks
Leverage point
Linear regression
Lists
Loss aversion
Loss severity
Louis Vuitton
M
Machine learning
real-time machine learning
self-improving machine learning
Mahalanobis distance
Manipulation
Marginal significance
Martians
Materiality
Mental fatigue
Meta data
Metrics
Missing at random
Missing data
Missing values
Model
Model assembly
Model design
Model development
Model documentation
Model estimation
Model implementation
Model inventory
Model tuning
Model validation
Monitoring
N
Natural language processing
Neural networks
Noise
Normal ranges
Nylon
O
Omission
Omitted data
Ordinary least squares (OLS)
Ordinary least squares (OLS) regression
Outlier
Overconfidence
Overfitting
Overlay
Overoptimism
Override analysis
P
Pearson’s product moment correlation
Police
Policemen
Population profile
Predictors
Prejudice
Principal Component Analysis (PCA)
Propensity of bias
Protected variables
Q
Q&A style model documentation
Qualitative data
Qualitative scorecard
Quotas
R
Random samples
Rank-ordering analysis
Rare cases
Ratios
Real-time machine learning
Real-world biases
Reject inference
Reputational risks
Response speed
Reverse-engineering of algorithms
Root cause analysis
S
Safe us of algorithms
Sample definition
Sample size
Scores
Search optimization
Second opinion
Seemingly quantitative data
Segmentation
Self-fulfilling bias
Self-improving algorithms
Shift effects
Short-listing of variables
Significance
Single truth
Social media
Socrates
Spearman’s rank correlation
Speed
Splitting of samples
Stability biases
Standardized inspection routine
Standards
Statistical algorithms
Stock numbers
Structural changes
Structure
Subjective data
Subsegments
Sunflower management
Systematic errors
T
Templates
model documentation template
Texas Sharpshooter fallacy
Through the door
Time window
Top-down
Traumatizing events
Tree-based models
Trip wires
U
Understanding algorithms
Univariate predictive power
Unknown values
V
Validation
Variable
dependent variables
independent variables
Variable transformation
Variance Inflation Factors (VIF)
Visual inspection
Von Restorff effect
W
Weight of Evidence (WOE)
X, Y
XAI
SeeExplainable artificial intelligence (XAI)
X-ray
Z
Zero values
Zeta Reticulans
..................Content has been hidden....................
You can't read the all page of ebook, please click here login for view all page.