Reliable Machine Learning

Index

A

• access, privacy preservation, Technical measures
• accuracy
  • classification metrics, Accuracy-Accuracy
  • discovery and definition phase, Discovery and Definition
  • validation and, Concrete recommendations
• active learning, AI and, Active Learning and AI-Assisted Labeling
• ad click prediction, case study, Background-Takeaways
• aggregation, monitoring systems, What Does It Look Like?
• Agile Manifesto, Agile ML?
• AI (artificial intelligence)
  • labeling and, Active Learning and AI-Assisted Labeling
  • Responsible AI, Responsible AI
    • effectiveness, Effectiveness-Effectiveness
    • explanations, Explanation-Explanation
    • social and cultural appropriateness, Social and Cultural Appropriateness
• alerting, What Is Production Monitoring and Why Do It?
• algorithmic bias, normative values and, Definitions of Fairness
• algorithmic fairness, Fairness (a.k.a. Fighting Bias), Reaching Fairness
  • crime-prediction algorithms, Fairness (a.k.a. Fighting Bias)
• algorithmic feature engineering, Feature Selection and Engineering
• Amazon Azure Machine Learning, Platforms
• Amazon Redshift, Storage
• Amazon SageMaker, Platforms
• analysis and visualization phase of data management, Analysis and Visualization
• annotation workforces, Annotation Workforces-Annotation Workforces
  • platform, An Annotation Platform
  • quality, Measuring Human Annotation Quality-Measuring Human Annotation Quality
• anonymization, Privacy
  • PII (personally identifiable information), Data as Liability
• anonymized data, Data as Liability
  • pseudonymized data, Data as Liability
  • raw data, Data as Liability ...