Bias in, Bias Out : Knowing various Biases in Testing AI | Maheshwaran VK | Testflix 2025
Just like humans, AI systems are shaped by how they are brought up. In the case of Large Language Models, this upbringing happens through data collection, training, and productization. At each of these stages, bias can quietly enter the system through the data we select, the way models are trained, or the assumptions embedded into the final product. These biases, whether intentional or accidental, influence how models think, respond, and interact with users in the real world.
This session explores the different points in the AI lifecycle where bias can be introduced and how it impacts the fairness and behavior of LLMs. While bias can never be fully eliminated, it can be consciously identified, reduced, and balanced. The talk shares practical approaches to managing bias during data sampling, training, and ongoing development, with the aim of building AI systems that are more responsible, transparent, and trustworthy.
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This video is of one of the sessions presented at #Testflix - World’s Leading Virtual Software Testing Conference.
About the Speaker:
Maheshwaran is a passionate software professional with a strong foundation in testing and a growing focus on Artificial Intelligence and Large Language Models. With several years of hands-on experience in building test automation frameworks and exploring AI-driven testing approaches, he brings a quality-first perspective to the evolving AI landscape.
He is currently exploring how models learn, how bias enters AI systems, and how teams can build more ethical and trustworthy solutions. Maheshwaran enjoys connecting complex technical ideas with simple, real-world analogies, making advanced concepts easier to understand and apply.
Connect with Maheshwaran on LinkedIn -
https://www.linkedin.com/in/satparkashmaurya/
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