Correlation vs Causation

Ice cream sales and drowning deaths both increase in summer. This doesn't mean ice cream causes drowning - both are caused by hot weather. This classic example illustrates a problem that's endemic in AI: systems learn correlations from data, but correlation is not causation. An AI might discover that people who buy premium pet food also tend to have higher credit scores, but that doesn't mean buying expensive dog food will improve your credit rating. Both are probably linked to income. This matters because AI systems trained on historical data absorb every correlation in that data, useful or spurious, and have no built-in way to distinguish between them. When you use AI for decision-making - hiring, lending, medical diagnosis, marketing - you need to think carefully about whether the patterns it's found reflect genuine causal relationships or just statistical coincidences. The technology for causal reasoning in AI is improving but still limited. For now, human judgement remains essential for asking the question that AI systems can't reliably answer on their own: why is this pattern there, and will it hold in the future?