From the course: Foundations of Responsible AI
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Engineers as ethics first responders
From the course: Foundations of Responsible AI
Engineers as ethics first responders
- When AI systems fail in the real world, the first signs are often subtle. A model starts behaving inconsistently with certain inputs, an anomaly appears in performance metrics, but it doesn't trigger an alert. And sometimes a dataset contains patterns that reflect more about the past than the future it's meant to inform. In moments like these, engineers are often the first to see what others will only notice later. You're working closest to the code. You understand how the system was designed, and you're in a unique position to recognize when it isn't doing what it should. This proximity gives you a kind of ethical visibility as well. A chance to notice, respond, and redirect before a harm gets locked in. It's a role that's rarely formalized, but it shows up often. A developer flags a skew in test results across demographic groups, or a machine learning engineer identifies a corner case that the product spec overlooked, or a team lead raises a question about what kind of feedback…
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