Bias and ML – is it possible to have an objective AI?
In the intertwined domains of ML and software engineering, the allure of data-driven decision-making and predictive modeling is undeniable. These fields, which once operated largely in silos, now converge in numerous applications, from software development tools to automated testing frameworks. However, as we increasingly rely on data and algorithms, a pressing concern emerges: the issue of bias. Bias, in this context, refers to systematic and unfair discrepancies that can manifest in the decisions and predictions of ML models, often stemming from the very data used in software engineering processes.
The sources of bias in software engineering data are multifaceted. They can arise from historical project data, user feedback loops, or even the design and objectives of the software itself. For instance, if a software tool is predominantly tested and refined using feedback from a specific demographic, it might inadvertently...
