Human Evaluation vs Quantitative Metrics
Developers should learn and use human evaluation when building systems where automated metrics are insufficient or misleading, such as in evaluating the fluency of generated text, the usability of a user interface, or the fairness of an AI model meets developers should learn and use quantitative metrics to improve software quality, enhance performance, and support evidence-based decision-making in projects. Here's our take.
Human Evaluation
Developers should learn and use human evaluation when building systems where automated metrics are insufficient or misleading, such as in evaluating the fluency of generated text, the usability of a user interface, or the fairness of an AI model
Human Evaluation
Nice PickDevelopers should learn and use human evaluation when building systems where automated metrics are insufficient or misleading, such as in evaluating the fluency of generated text, the usability of a user interface, or the fairness of an AI model
Pros
- +It is essential in research and development phases to ensure that outputs align with human expectations and ethical standards, particularly in applications like chatbots, content generation, and recommendation systems
- +Related to: user-experience-testing, machine-learning-evaluation
Cons
- -Specific tradeoffs depend on your use case
Quantitative Metrics
Developers should learn and use quantitative metrics to improve software quality, enhance performance, and support evidence-based decision-making in projects
Pros
- +Specific use cases include monitoring application performance with metrics like latency and throughput, measuring code quality with test coverage and defect density, and tracking team productivity using velocity or cycle time in agile workflows
- +Related to: data-analysis, performance-monitoring
Cons
- -Specific tradeoffs depend on your use case
The Verdict
Use Human Evaluation if: You want it is essential in research and development phases to ensure that outputs align with human expectations and ethical standards, particularly in applications like chatbots, content generation, and recommendation systems and can live with specific tradeoffs depend on your use case.
Use Quantitative Metrics if: You prioritize specific use cases include monitoring application performance with metrics like latency and throughput, measuring code quality with test coverage and defect density, and tracking team productivity using velocity or cycle time in agile workflows over what Human Evaluation offers.
Developers should learn and use human evaluation when building systems where automated metrics are insufficient or misleading, such as in evaluating the fluency of generated text, the usability of a user interface, or the fairness of an AI model
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