• Machine learning testing: Survey, landscapes and horizons
• Quality Assurance for AI-based Systems: Overview and Challenges
• Reliable Machine Learning: Applying SRE Principles to ML in Production [BOOK
• Metamorphic testing of decision support systems: A case study
• A Survey on Metamorphic Testing
• Machine learning testing: Survey, landscapes and horizons
• Quality Assurance for AI-based Systems: Overview and Challenges
• Reliable Machine Learning: Applying SRE Principles to ML in Production [BOOK
• Metamorphic testing of decision support systems: A case study
• A Survey on Metamorphic Testing
• Testing and validating machine learning classifiers by metamorphic testing
• Testing and validating machine learning classifiers by metamorphic testing
• The Disagreement Problem in Explainable Machine Learning: A Practitioner’s Perspective
• InterpretML: A Unified Framework for Machine Learning Interpretability
• Fair regression: Quantitative definitions and reduction-based algorithms
• The Disagreement Problem in Explainable Machine Learning: A Practitioner’s Perspective
• Fair regression: Quantitative definitions and reduction-based algorithms
• Learning Optimal and Fair Decision Trees for Non-Discriminative Decision-Making
• Towards the Systematic Reporting of the Energy and Carbon Footprints of Machine Learning
• InterpretML: A Unified Framework for Machine Learning Interpretability
• Learning Optimal and Fair Decision Trees for Non-Discriminative Decision-Making
• Towards the Systematic Reporting of the Energy and Carbon Footprints of Machine Learning

AI Incident Databases

• AI Incident Database
• AI Vulnerability Database
• AI Vulnerability Database
• AI Incident Database

AI Incident Databases

• Machine Learning Model Drift Detection Via Weak Data Slices
• Automated Data Slicing for Model Validation: A Big Data - AI Integration Approach
• Machine Learning Model Drift Detection Via Weak Data Slices
• Automated Data Slicing for Model Validation: A Big Data - AI Integration Approach
• Interacting with Predictions: Visual Inspection of Black-box Machine Learning Models