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The TAILOR Handbook of Trustworthy AI
The TAILOR Handbook of Trustworthy AI
The Ethical and Legal Framework
Ethics Guidelines for Trustworthy AI by High-Level Expert Group on Artificial Intelligence
The EU AI Act
Prohibited AI Practices
High Risk AI Systems
Trustworthy AI
Human Agency and Oversight
Meaningful human control
Causal Responsibility
Transparency
Dimensions of Explanations
Black Box Explanation vs Explanation by Design
Model-Specific vs Model-Agnostic Explainers
Global vs Local Explanations
Explainable AI
Kinds of Explanations
Technical Robustness and Safety
Alignment
Robustness
Reliability
Evaluation
Negative side effects
Distributional shift
Security
Adversarial Attack
Data Poisoning
Uncertainty
Diversity, Non-Discrimination, and Fairness
Auditing AI
Bias
Bias Conducive Factors
Bias and Fairness in LLMs
Discrimination & Equity
Fairness notions and metrics
Fair Machine Learning
Grounds of Discrimination
Intersectionality
Justice
Segregation
Accountability
Accountability
Wicked problems
The Frame Problem
The Problem of Many Hands
Reproducibility
Traceability
Provenance Tracking
Continuous Performance Monitoring
Privacy and Data Governance
Data Anonymization (and Pseudonymization)
Pseudonymization
Privacy Models
Randomization Methods
Differential Privacy
Anonymity by Indistinguishability
Federated Learning
Attacks on anonymization schemes
Re-identification Attack
Societal and Environmental Wellbeing
Sustainable AI
Green AI
Cloud Computing
Edge Computing
Data Centre
Cradle-to-cradle Design
Resource Prediction
Resource Allocation
Social Impact of AI Systems
AI human interaction
AI Impact on the Workforce
Society and Democracy
AI for social scoring
AI for propaganda
The TAILOR project
Complete List of Contributors
Index
2CC2
Accountability
AI for propaganda
AI for social scoring
AI human interaction
AI Impact on the Workforce
Alignment
Adversarial Attack
Adversarial Example
Adversarial Input
Anonymity by Indistinguishability
Ante-hoc Explanation
Assessment
Attacks on Anonymization Schema
Attacks on Pseudonymised Data
Auditing
Bias
Bias Conducive Factors
Bias and Fairness in LLMs
Black-box Explanation
Brittleness
C2C
Causal Responsibility
Cloud Computing
Continuous Performance Monitoring
Counterexemplars
Counterfactuals
Cradle 2 cradle
Cradle-to-cradle Design
Data Anonymization
Data Center
Data Poisoning
Dependability
Differential Privacy Models
Emotional Impact
(
\(\epsilon\)
,
\(\delta\)
)-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
Exemplars
Explainable AI
Explanation by Design
Fair Machine Learning
Fairness
Feature Importance
Federated Learning
The Frame Problem
Fog Computing
Model Agnostic
Global Explanations
Green AI
Green Computing
Green IT
ICT sustainability
Intended Behaviour
Intersectionality
Justice
K-anonymity
l-diversity
Linking Attack
Local Explanations
Local Rule-based Explanation
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
Problem of Many Hands
Prototypes
Provenance Tracking
Pseudonymization
Randomization Methods
Re-identification Attack
Regenerative Design
Reliability
Repeatability
Replicability
Reproducibility
Resource Allocation
Resource Prediction
Resource Scheduling
Robustness
Rules List and Rules Set
Saliency Maps
Security
Segregation
Self-identification of AI
Single Tree Approxiamation
Social Impact of AI Systems
Society and Democracy
t-closeness
Testing
Traceability
Transparency
Unintended Behaviour
XAI
Wicked Problems
Workload Forecast
Workload Prediction
repository
open issue
Index
