Sammy Kolluru

Managing Director

Join my presentation on: Testing AI-Based Systems: Considerations Quality, Risk, and Ethical Assurance

The growing adoption of artificial intelligence in software-intensive systems introduces new challenges for software testing that extend beyond the scope of traditional software testing practices. AI-based systems are inherently data-driven, adaptive, and probabilistic, making them susceptible to risks such as data bias, model drift, unstable performance metrics, and ethical concerns related to fairness and transparency. 
 
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Who is Sammy Kolluru ?

Managing Director

Sammy Kolluru, Managing Director of imbus Canada Corporation and a seasoned professional with over 20 years of experience in Software Testing and Quality Assurance.
 
Sammy has been pivotal in establishing imbus Canada as a leading solutions partner in software testing and quality assurance. His commitment to delivering high-quality software is underscored by his focus on effectiveness, efficiency, measurement, and visibility. His leadership prowess is further evidenced by his role as a founding member and current Board Director of TMMi America and his contributions to the ISTQB.

What will Sammy Kolluru be discussing?

Testing AI-Based Systems: Considerations Quality, Risk, and Ethical Assurance

The growing adoption of artificial intelligence in software-intensive systems introduces new challenges for software testing that extend beyond the scope of traditional software testing practices. AI-based systems are inherently data-driven, adaptive, and probabilistic, making them susceptible to risks such as data bias, model drift, unstable performance metrics, and ethical concerns related to fairness and transparency. 
 
This presentation discusses the specific requirements for testing AI-based systems through the lens of test maturity. It discusses the limitations of conventional test techniques and coverage models when applied to AI components and highlights the need for revised testing strategies, specialized test objectives, and continuous monitoring mechanisms. 
 
By referencing maturity-oriented frameworks such as TMMi, the presentation outlines how organizations can progressively integrate AI-specific considerations—such as data validation, model performance tracking, and ethical risk management—into their testing processes. 

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