Thumbs-Down Rate Measure

A Thumbs-Down Rate Measure is an explicit feedback measure that quantifies the percentage of AI responses receiving negative thumbs-down signals from system users.

• AKA: Negative Feedback Rate, Dislike Rate, Downvote Rate, Explicit Disapproval Rate.
• Context:
  • It can typically identify Response Quality Problems through user dissatisfaction signals with failure pattern recognition.
  • It can often trigger Immediate Improvement Actions via feedback loop integration with alert systems.
  • It can measure Error Severity by correlating with session abandonment and user churn.
  • It can detect Model Degradation through temporal trend monitoring with performance baseline comparison.
  • It can categorize Failure Modes through feedback reason collection with problem taxonomy.
  • It can inform Priority Bug Fixes through impact assessment with frequency analysis.
  • It can exhibit Negativity Bias where users more likely provide negative feedback than positive feedback.
  • It can range from being a Very Low Thumbs-Down Rate Measure to being a Low Thumbs-Down Rate Measure, depending on its system quality.
  • It can range from being a Moderate Thumbs-Down Rate Measure to being a High Thumbs-Down Rate Measure, depending on its problem frequency.
  • It can range from being a Critical-Error Thumbs-Down Rate Measure to being a Minor-Issue Thumbs-Down Rate Measure, depending on its severity level.
  • It can range from being a Consistent Thumbs-Down Rate Measure to being a Spike-Pattern Thumbs-Down Rate Measure, depending on its temporal distribution.
  • It can range from being a Feature-Specific Thumbs-Down Rate Measure to being a System-Wide Thumbs-Down Rate Measure, depending on its scope.
  • ...
• Example(s):
  • AI Code Generator Thumbs-Down Rate, such as:
    • 15% thumbs-down for syntax errors in generated code.
    • 25% thumbs-down for inefficient algorithms.
  • Translation AI Thumbs-Down Rate, such as:
    • 10% thumbs-down for minor grammar issues.
    • 40% thumbs-down for context misunderstandings.
  • Medical AI Assistant Thumbs-Down Rate, such as:
    • 5% thumbs-down for conservative recommendations.
    • 30% thumbs-down for incomplete information.
  • ...
• Counter-Example(s):
  • Thumbs-Up Rate Measure, which tracks positive feedback rather than negative signals.
  • Error Rate Measure, which measures technical failures rather than user disapproval.
  • Bounce Rate Measure, which tracks behavioral signals rather than explicit ratings.
• See: Explicit Feedback Measure, Thumbs-Up Rate Measure, Answer Success Rate Measure, User Dissatisfaction Score, Response Quality Metric, Error Detection System, Session Abandon Rate Measure, AI System Bounce Rate Measure.

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