Part 9/14:
He underscores that AI introduces risks, particularly when training data is biased or unrepresentative. For example, training models solely on data from specific regions or political viewpoints can result in biased outputs, potentially leading to unethical or inaccurate decisions. Due diligence requires careful selection and validation of data sets, awareness of biases, and thoughtful consideration of the social implications—especially in sensitive fields like healthcare.
Cooper advocates for a disciplined approach: use AI only for problems that are complex, high-value, and supported by robust, well-curated data. When applied judiciously, AI enhances decision quality and operational efficiency without introducing new risks.