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Quality assurance is the overarching term used to describe the activities employed to ensure the proper performance of a system. These activities can be classified into three categories: quality control, quality assessment, and internal corrective measures. Typically, these activities work cyclically: quality control is performed before and during the analysis, while quality assessment occurs during and after the investigation. Internal corrective measures are implemented based on the findings...
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Yes we care!-Certification for machine learning methods through the care label framework.

Katharina J Morik1, Helena Kotthaus1, Raphael Fischer1

  • 1Faculty of Computer Science, TU Dortmund University, Dortmund, Germany.

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|October 10, 2022
PubMed
Summary
This summary is machine-generated.

We introduce a novel framework using "care labels" to certify machine learning models, ensuring guaranteed properties for users without requiring deep technical knowledge. This approach bridges the gap between complex AI theory and practical, trustworthy deployment.

Keywords:
care labelscertificationprobabilistic graphical modelstesting machine learningtrustworthy AI

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning Engineering

Background:

  • Machine learning (ML) is widely used across various domains, necessitating trustworthy applications.
  • Existing efforts in explainable and fair AI cater to expert users.
  • A gap exists for end-users seeking guaranteed ML model properties without technical expertise.

Purpose of the Study:

  • To develop a unified framework for certifying machine learning methods.
  • To provide easily understandable property guarantees for non-expert stakeholders.
  • To ensure the reliability of deployed ML models through verifiable properties.

Main Methods:

  • Proposed a certification framework inspired by textile care labels and electronic device property cards.
  • Developed
  • care labels
  • to express ML model properties in an accessible manner.
  • Integrated ML theory with implementation analysis to verify compliance.

Main Results:

  • The framework offers a novel approach to certifying ML methods.
  • Care labels provide understandable guarantees for stakeholders.
  • The approach validates ML implementation compliance with theoretical properties.

Conclusions:

  • The proposed care label framework enhances trustworthiness in machine learning deployments.
  • It simplifies the communication of complex ML properties to a broader audience.
  • This work advances the state-of-the-art in creating reliable and user-friendly AI systems.