Toggle navigation sidebar
Toggle in-page Table of Contents
The TAILOR Handbook of Trustworthy AI
The TAILOR Handbook of Trustworthy AI
Complete List of Contributors
Explainable AI Systems
Kinds of Explanations
Feature Importance
Saliency Maps
Single Tree Approximation
Dimensions of Explanations
Black Box Explanation vs Explanation by Design
Model-Specific vs Model-Agnostic Explainers
Global vs Local Explanations
Safety and Robustness
Alignment
Robustness
Reliability
Evaluation
Negative side effects
Distributional shift
Security
Adversarial Attack
Data Poisoning
Fairness, Equity, and Justice by Design
Auditing AI
Bias
Discrimination & Equity
Fairness notions and metrics
Fair Machine Learning
Grounds of Discrimination
Justice
Segregation
Accountability and Reproducibility
Accountability
Wicked problems
Meaningful human control
The Frame Problem
Reproducibility
Traceability
Provenance Tracking
Continuous Performance Monitoring
Respect for Privacy
Data Anonymization (and Pseudonymization)
Pseudonymization
Privacy Models
Differential Privacy
\(\epsilon\)
-Differential Privacy
(
\(\epsilon\)
,
\(\delta\)
)-Differential Privacy
Achieving Differential Privacy
k-anonymity
Attacks on anonymization schemes
Re-identification Attack
Sustainability
Green AI
Power-aware Computing
Cloud Computing
Edge Computing
Data Centre
Cradle-to-cradle Design
Resource Prediction
Resource Allocation
About TAILOR
Index
2CC2
Accountability
Alignment
Adversarial Attack
Adversarial Example
Adversarial Input
Ante-hoc Explanation
Assessment
Attacks on Anonymization Schema
Attacks on Pseudonymised Data
Auditing
Bias
Black-box Explanation
Brittleness
C2C
Cloud Computing
Continuous Performance Monitoring
Cradle 2 cradle
Cradle-to-cradle Design
Data Anonymization
Data Center
Data Poisoning
Dependability
Differential Privacy Models
(
\(\epsilon\)
,
\(\delta\)
)-Differential Privacy
\(\epsilon\)
-Differential Privacy
\(\epsilon\)
-Indistinguishability
Distributional Shift
Dimensions of Explanations
Grounds of Discrimination
Distributional Shift
Direct Behaviour
Edge Computing
Energy-aware Computing
Energy-efficient Computing
Evaluation
Equity
Explanation by Design
Fair Machine Learning
Fairness
Feature Importance
The Frame Problem
Fog Computing
Model Agnostic
Global Explanations
Green AI
Green Computing
Green IT
ICT sustainability
Intended Behaviour
Justice
K-anonymity
Linking Attack
Local Explanations
Meaningful Human Control
Measurement
Mesh Computing
Misdirect Behaviour
Model Agnostic
Model Specific
Negative Side Effects
Model Specific
Achiving Differential Privacy
Post-hoc Explanation
Power-aware Computing
Privacy models
Provenance Tracking
Pseudonymization
Re-identification Attack
Regenerative Design
Reliability
Repeatability
Replicability
Reproducibility
Resource Allocation
Resource Prediction
Resource Scheduling
Robustness
Saliency Maps
Security
Segregation
Single Tree Approxiamation
Testing
Traceability
Transparency
Unintended Behaviour
Wicked Problems
Workload Forecast
Workload Prediction
repository
open issue
Index