Learn practical engineering approaches to responsible AI, covering governance, bias, explainability, privacy, automation bias, and human oversight.

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The article examines responsible AI through the lens of software engineering rather than philosophy. It explores accountability, algorithmic bias, explainability, data privacy, automation bias, governance, and human-AI collaboration, arguing that trustworthy AI depends on well-designed systems, clear ownership, continuous monitoring, and deliberate oversight.