THE AI INTERVIEW
Alice contends that the deeper issue lies further upstream – in the datasets used to build these systems in the first place.
“ These issues almost always originate in the data,” she continues.“ Training data forms the foundation of every AI system, and if that foundation is biased or unrepresentative, those flaws will appear in the model’ s behaviour.”
Part of the problem, according to Alice, is a lack of clear standards and benchmarks for collecting data responsibly.
“ In fields such as computer vision,” she says,“ technical innovation has moved faster than ethical guidance. This has resulted in datasets that may lack diversity, reflect societal biases or be collected without adequate consent.”
Alice points to verification systems and facial-recognition technology as areas where fairness and accuracy carry realworld consequences, adding:“ Without high-quality, responsibly-sourced datasets, even the most advanced models will reproduce the limitations of the data they were built on.”
22 July 2026