From the course: Foundations of Responsible AI
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The business case for early integration of responsible AI
From the course: Foundations of Responsible AI
The business case for early integration of responsible AI
- It's easy to think of responsibility as something we do after the fact to revisit. Once the architecture's set and the model is trained and the product's about to launch, but in practice deferring those questions creates more work, not less. The earlier teams engage with ethical and social risks, the fewer costly surprises come later across sectors. The failures we see most often in AI systems aren't due to obscure engineering flaws. No, they reflect design choices that went unexamined for too long. Credit scoring tools that replicate past discrimination. Content moderation systems that struggle to understand cultural nuance, or large language models that produce confident answers but are unsupported by facts. These outcomes could have been predictable and in many cases they could have been addressed before deployment with far less disruption. So when responsibility is treated as a last step, the results usually a scramble. Teams are forced to retrofit explanations and then try to…
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