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awesome-ai-safety
📚 A curated list of papers & technical articles on AI Quality & Safety
https://github.com/giskard-ai/awesome-ai-safety
Last synced: 1 day ago
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General ML Testing
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- 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
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AI Incident Databases
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Tabular Machine Learning
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AI Incident Databases
- 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
- 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
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