Experts Chris, Kiran, and others will discuss quality engineering’s role in AI development. 
Learn about quality assurance, ethical considerations, and testing frameworks to manage AI risks and gain insights into best practices and challenges in AI technologies.
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Hi there,
If you couldn’t catch the session live, don’t worry! You can watch the recording here:
Additionally, we’ve got you covered with a detailed session blog:
Here are some unanswered questions that were asked in the session:
With the increasing integration of AI in quality engineering, what strategies should be adopted to ensure that AI-driven testing maintains high standards of reliability and minimizes the risk of introducing new types of errors or biases?
What future trends do you foresee in the integration of AI and quality engineering, and how can engineers prepare for them?
A query - How safe is to put your user story description and detail on generative AI tools, to generate the test scenarios and cases ? How shall we ensure the content we search remains secure and our product/project related secrets are not revealed ?
Love what Subba is talking about! I think it’s crucial to stop for just a second and assess these aspects of testing AI as prio one! How can this put into general practice?
What responsibilities do quality engineers have in ensuring AI systems operate ethically?
In the dynamic and evolving nature of AI models, how we as quality engineering team can keep up with the scalability challenges in testing AI systems that continuously learn and adapt?
How do we adapt traditional validation and verification processes when dealing with AI systems, especially when the AI’s decision-making is not entirely transparent or deterministic?
Tell Something about how can organisation measure the quality engineers??
What kind of guardrails are required for not just steering AI but also complying with governance/regulations applicable to quality engineering?
Why is Quality Engineering pivotal in steering AI projects towards successful outcomes?
What best practices should we follow to ensure that the AI models we develop or test are accurate, reliable, and free from biases? How does quality engineering contribute to this?
What challenges do testers face when implementing AI, and how can they overcome them?
I can’t help but wonder…How much sensitive data do we estimate is already out there, that folks have input in error or out of ignorance, which if exposed to the public would maybe even take down entire companies?
Is there any course available for learning QA for AI applications?
What skills do we need to learn as QA professionals to effectively guide the development team and product to deliver a quality AI product?
How can organizations measure the impact of Quality Engineering on the overall success of AI implementations?
What are the unique challenges in testing AI models, and how can quality engineers address issues like unpredictability and bias in AI outputs?
What are the recommended AI tools to integrate with Selenium and Appium using Java?
