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KI og overholdelse - Ytelse og nøyaktighet (AI and Compliance- Performance and Accuracy)

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Evalueringsberegninger

Hvilke kriterier vil bli brukt for å evaluere ytelsen og nøyaktigheten til AI-sammendragene?

Ytelsen og nøyaktigheten til KI-sammendragene vil bli evaluert ved hjelp av følgende kriterier:

  • Presisjon: Andelen av relevant informasjon som er korrekt identifisert i sammendragene.

  • Tilbakekalling: Andelen av all relevant informasjon som fanges opp i sammendragene.

  • F1-poengsum: Det harmoniske gjennomsnittet av presisjon og gjenkalling gir et enkelt kriterie for total nøyaktighet.

  • Brukertilfredshet: Tilbakemeldinger fra brukere angående nytten og relevansen av sammendragene.

  • Sammenligning mot referansemålinger: Sammenligning av KI-genererte sammendrag mot menneskegenererte referansemålinger for å sikre kvalitet.

🇬🇧 AI and Compliance- Performance and Accuracy

Evaluation Metrics

 

What metrics will be used to evaluate the performance and accuracy of the AI summaries?

The performance and accuracy of the AI summaries will be evaluated using the following metrics:

  • Precision:The proportion of relevant information correctly identified in the summaries.

  • Recall: The proportion of all relevant information that is captured in the summaries.

  • F1 Score: The harmonic mean of precision and recall, providing a single metric for overall accuracy.

  • User Satisfaction: Feedback from users regarding the usefulness and relevance of the summaries.

  • Comparison to Benchmarks: Comparison of AI-generated summaries against human-generated benchmarks to ensure quality.

Continuous Monitoring

 

How will we monitor the AI system's performance over time to ensure consistent quality and accuracy?

Continuous monitoring will be implemented through:

  • Automated Monitoring: Automated systems to track the performance metrics and alert the team to any significant changes or declines in quality.

  • Regular Performance Reviews: Scheduled performance reviews to analyse metrics and make necessary adjustments.

  • User Feedback Analysis: Ongoing collection and analysis of user feedback to identify areas for improvement.

  • Performance Logs: Detailed logs of system performance over time, allowing for trend analysis and proactive adjustments.

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