Quality assurance is often overlooked in the dynamic domain of machine learning and artificial
intelligence. Pradeesh Ashokan, a Senior IEEE Panel Reviewer at IEEE SoutheastCon 2025 and a
Bronze Winner for Customer Excellence at Globee® Awards emphasises the critical role of Quality
Assurance which he has been mastering over the last six years at his various stints.
AI is percolating in almost every industry that one can think of, from healthcare to smart devices and
voice assistants like Amazon’s Alexa. Pradeesh has been working on testing complex AI and ML systems. He has worked with Machinify, Viv Labs with Siri Co- Founders and Riva Health. He said, “All technical professionals have a role to play and contribute to quality assurance for pioneering technologies”.
Quality Assurance directed to Real World Scenarios
AI and ML rely on probabilistic models and vast datasets making it significantly different from
traditional software which throws up multilayered challenges for testing as well. Speaking about the
importance of quality assurance, Pradeesh stated, “The industry is rife with discussions on efforts to
ensure rigorous testing models for vulnerabilities, potential hacking capabilities so as to prevent
misuse. Before deploying AI, it is essential to do a fool proof evaluation”.
While explaining the complexities of AI testing, he added, “Testing AI is not just about calculations
and functional correctness, it is far more to do with the impact and the end result. The models must
work in the dynamic real-world. The prima facie challenge in testing AI lies in its inherent nature of
variability’.
When Quality Assurance is Non-Negotiable
In certain industries such as healthcare and finance, quality assurance assumes a far more
fundamental role. These industries operate within highly regulated environments with layered
compliances. Ashokan opined, “Safety and trust are cornerstones of the healthcare and finance
industries. If we take the example of AI-driven healthcare solutions, such as ML-based
cardiovascular health monitoring systems or healthcare infrastructure that assists in various
surgeries we have to ensure accuracy, hence QA becomes non-negotiable”.
Collaboration, Automation and Knowledge Sharing for Scalability.
Pradeesh who has been featured on technology journals like Nanotechnology and SARC believes that
the future of Quality Assurance is hinged on collaboration, automation and knowledge sharing. He
said, “The pressure of AI/ML needs are increasing and evolving every day. New challenges are
thrown up constantly. We need to embark upon robust testing, deep dive into complex test
scenarios and automate test suites to validate algorithms. Especially when AI models are used for
critical decision making. The industry has to unite to keep bettering the outcomes, we have achieved
95%-96 % accuracy in predicting medical treatment outcomes, we have to strive to achieve a 100%
success rate”.
Pradeesh emphasizes on building a community and an eco-system for professionals to collaborate
and grow. A recipient of the 2018 Veey Cup Award, Ashokan concluded,” We have to build trust at
all levels. End users off course but developers and business as well. It is our responsibility to not just
accomplish the job at hand but contribute to the growth of technology and that can happen only through collaboration and knowledge sharing.
