AI bias: 9 questions leaders should ask

As the use of artificial intelligence applications – and machine learning – grows within businesses, government, educational institutions, and other organizations, so does the likelihood of bias. Researchers have studied and found significant racial bias in facial recognition technology, for example, and in particular in the underlying algorithms. That alone is a massive problem. When you more broadly consider the role AI and ML will play in societal and business contexts, the problem of AI bias becomes seemingly limitless – one that IT leaders and others need to pay close attention to as they ramp up AI and ML implementations. AI bias often begins with people, which runs counter to the popular narrative that we'll all soon be controlled by AI robot overlords.