Building Trustworthy AI

Coursera MOOC / Non-credit USD 49
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Building Trustworthy AI

About this course

As AI systems become critical infrastructure powering businesses worldwide, ensuring their trustworthiness is essential. This comprehensive specialization equips you with the complete toolkit to build, secure, and govern AI systems that are ethical, transparent, and resilient against emerging threats. You'll journey through the entire AI trustworthiness spectrum: from identifying and mitigating AI-specific security vulnerabilities across the MLOps lifecycle, to implementing enterprise-grade governance frameworks that balance innovation with responsibility. Through hands-on labs and real-world scenarios, you'll learn to threat-model AI endpoints, conduct ethical audits, design reward systems that align with human values, and establish monitoring systems that ensure consistent performance and fairness. This specialization uniquely combines technical security expertise with ethical governance, preparing you to lead responsible AI initiatives. Whether you're securing inference endpoints against prompt injection attacks, implementing explainability tools like SHAP and LIME, or creating risk management frameworks aligned with NIST standards, you'll gain immediately applicable skills that address today's most pressing AI deployment challenges. Perfect for security professionals, ML engineers, compliance officers, and technical leaders who recognize that the future of AI depends not just on what we can build, but on what we should build—and how to protect it.

What you'll learn

  • identify and mitigate AI-specific security vulnerabilities
  • implement governance frameworks for ethical AI practices
  • conduct ethical audits of AI systems
  • design reward systems aligning AI outcomes with human values
  • establish monitoring systems for performance and fairness

Course objectives

  • prepare professionals to lead responsible AI initiatives
  • equip learners with technical security expertise in AI

Skills you'll gain

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