Abstract:

The incorporation of AI into testing methodologies has brought about a paradigm shift, improving accuracy, efficiency, and scalability. On the other hand, due to the ever-changing and intricate nature of AI systems, a strong quality assurance framework is needed to tackle issues like algorithmic bias, lack of transparency, and ethical compliance. In this study, we look at the key elements of such a framework, including algorithm validation, data integrity, performance metrics, and ethical oversight. By establishing common rules and encouraging openness, this framework hopes to guarantee the reliability and trust in AI-driven testing processes, encouraging innovation in various industries.

Description:

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