How do AI Engineers address bias in AI models?

AI Engineers assess bias in the sampling and pre-processing of activity data by ensuring that the data used in those activities is a mixed set and representative of various groups in society. The means or techniques to train the model are without bias: re-sampling, weighting, and adversarial training.

They will also conduct periodic model audits to identify and mitigate such bias arising over time. Through an insistence on fairness and transparency in model development, AI Engineers end up building AI systems resulting in fair outputs.