Dataset Quality Assurance Process

A Dataset Quality Assurance Process is a quality assurance process that supports dataset quality assurance tasks.

• AKA: Dataset QA Process, Data Quality Control Process, Dataset Validation Process.
• Context:
  • It can typically validate Dataset Completeness through dataset coverage analysis.
  • It can typically assess Dataset Consistency via dataset integrity checks.
  • It can often detect Dataset Bias using dataset statistical analysis.
  • It can often ensure Dataset Annotation Quality through dataset label validation.
  • ...
  • It can range from being a Manual Dataset Quality Assurance Process to being an Automated Dataset Quality Assurance Process, depending on its dataset QA automation level.
  • It can range from being a Sampling-Based Dataset Quality Assurance Process to being a Exhaustive Dataset Quality Assurance Process, depending on its dataset QA coverage.
  • It can range from being a Single-Stage Dataset Quality Assurance Process to being a Multi-Stage Dataset Quality Assurance Process, depending on its dataset QA complexity.
  • It can range from being a Static Dataset Quality Assurance Process to being a Continuous Dataset Quality Assurance Process, depending on its dataset QA frequency.
  • It can range from being a Domain-Agnostic Dataset Quality Assurance Process to being a Domain-Specific Dataset Quality Assurance Process, depending on its dataset QA specialization.
  • ...
  • It can integrate with Dataset Creation Processes for dataset quality control.
  • It can support ML Model Training through dataset reliability assurance.
  • It can enable Dataset Certification via dataset quality metrics.
  • ...
• Example(s):
  • Golden Dataset Quality Assurance Processes, such as:
    • Contract Review Golden Dataset Creation Process with contract review quality checks.
    • Medical Golden Dataset QA Process for clinical data validation.
  • Training Dataset QA Processes, such as:
    • Image Dataset Quality Assurance for computer vision applications.
    • Text Dataset Quality Assurance for NLP model training.
  • Benchmark Dataset QA Processes, such as:
    • Evaluation Dataset Validation for model testing.
    • Competition Dataset QA for ML challenges.
  • ...
• Counter-Example(s):
  • Data Collection Process, which gathers but doesn't assure quality.
  • Model Validation Process, which tests models not datasets.
  • Random Sampling Process, which selects without quality checks.
• See: Quality Assurance Process, Dataset Validation, Data Quality Metric, ML Dataset Management, Data Integrity Check, Annotation Quality Control.

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