What is the main strategy to reduce bias in AI systems?

Using diverse, balanced datasets and conducting external validation is crucial for reducing bias in AI systems. A diverse dataset ensures that the AI is trained on a wide range of inputs, reflecting different demographics, conditions, and scenarios. This variety helps the model see the broader picture and prevents it from inadvertently favoring one group over another. For instance, in a dentistry context, a system trained on a non-representative population might not perform well for individuals from different ethnic backgrounds or with varying dental conditions.

External validation is equally important, as it allows for the evaluation of the AI system’s performance on new, unseen data that wasn't part of the training set. This step helps identify any persistent biases that may affect the output and ensures that the model can generalize well to real-world situations, enhancing its reliability and fairness.

While other strategies, such as increasing processing speed or implementing constant updates, might enhance the functionality and responsiveness of AI systems, they do not inherently address the issue of bias. Limiting access to data sources could potentially reduce the richness of the training data and increase the likelihood of bias in the AI outcomes as well. Thus, the combination of using a diverse dataset and validating externally stands out as the most effective strategy to combat bias in AI systems