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The Data Bias
Track Talk I November 14 I Track 1 I 1:45 PM – 2:25 PM
Description
In our profession as testers, analysts, and developers, we try to base our decisions on objectivity. On data. Unfortunately, the information or data we base our decisions on is often biased. There are many reasons behind these biases, but the most striking one is a gender bias. A gender bias that is actually confirmed by a data bias. There are numerous examples of products being designed (IT and non-IT related), where a data bias results in a gender bias – with a negative effect on the quality, and on the uptake. Where does that put us as testers? What can we undertake to avoid releasing products that are not designed for the audience we are targeting? This track talk provides examples of the presence of data and gender biases, and how they result in negative consequences. I will also show you techniques to detect the biases, plus tools and best practices to avoid them.
Key-takeaways
- Your bias will influence your test cases
- GenAI reinforces data and gender bias
- There are ways to overcome these biases and provide deeper/bettre testing
About the Speaker
Breaking the system, helping to rebuild it, and providing advice and guidance on how to avoid problems. That’s Michaël in a nutshell. With over 20 years of experience in test consultancy in a variety of environments, he has seen the best (and worst) in software development. In his current role as Learning & Development Manager, he’s responsible for guiding our consultants, partners, and customers on their personal and professional path towards excellence. Michaël is the chair of the ISTQB Advanced workgroup, author and international keynote speaker
