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AI testing, evaluation, verification and validation for accessibility: a comprehensive framework.

Gabriella Waters1,2

  • 1Cognitive and Neurodiversity AI Lab, Center for Responsible AI, Virginia State University, Baltimore, VA, United States.

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Summary
This summary is machine-generated.

This study introduces an AI Testing, Evaluation, Verification and Validation (TEVV) framework to ensure artificial intelligence (AI) systems are accessible. Evaluating AI for accessibility barriers and biases enhances inclusivity for all users.

Keywords:
AI evaluationAI evaluation frameworkAI testingaccessibility (for disabled)artificial intelligence

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Area of Science:

  • Computer Science
  • Human-Computer Interaction
  • Artificial Intelligence Ethics

Background:

  • Artificial intelligence (AI) systems are increasingly integrated into society.
  • Ensuring AI accessibility for individuals with disabilities is crucial for equitable technology adoption.

Purpose of the Study:

  • To present a comprehensive framework for AI Testing, Evaluation, Verification and Validation (TEVV) specifically focused on accessibility.
  • To improve the inclusivity and effectiveness of AI technologies for diverse user populations.

Main Methods:

  • Developed a TEVV framework incorporating red teaming, model testing, and field testing.
  • Emphasized usability testing tailored for accessibility.
  • Conducted detailed case studies to validate the framework.

Main Results:

  • Systematic evaluation using the framework identified accessibility barriers and biases in AI systems.
  • The framework demonstrated improvements in AI inclusivity and effectiveness for diverse users.
  • Case studies provided empirical evidence of the framework's utility.

Conclusions:

  • The accessibility-focused TEVV framework offers a structured approach to developing equitable AI.
  • Implementing this framework leads to universally usable AI systems.
  • This methodology supports the creation of AI that benefits all members of society.