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Foundations of Ethical and Safety-Critical AI Engineering

Introduction to Ethics, Safety, and Verification in AI Engineering

Characteristics of Safety-Critical AI Systems

Ethical Concerns in Engineering AI Applications

Uncertainty Quantification and Risk-Aware Decision-Making

Sources and Types of Uncertainty in AI Models

Methods for Estimating and Communicating Uncertainty

Risk Assessment and Safe Operational Boundaries

Robustness Against Adversarial Inputs and Operational Failures

Robustness in AI-Enabled Engineering Systems

Adversarial Inputs and Threat Modeling

Defenses, Monitoring, and Resilience Strategies

Explainable AI and Human-Centered Assurance

Principles and Purposes of Explainable AI

Explanation Methods and Their Engineering Uses

Human Oversight and Effective AI Communication

Verification, Validation, and Standards Compliance

Verification and Validation for AI Systems

Testing Strategies for Safety-Critical AI

Engineering Standards, Compliance, and Assurance Cases

Integrated Responsible AI Deployment

Lifecycle Governance for Engineering AI

Capstone Scenario: Assessing an AI-Assisted Inspection System

Quiz

Quiz Question 1: Safety-Critical AI

Quiz Question 2: Uncertainty Quantification

Quiz Question 3: Adversarial Robustness

Quiz Question 4: Explainable AI

Quiz Question 5: Verification and Compliance

Survey and Feedback

Course Feedback Survey

As artificial intelligence becomes increasingly integrated into critical engineering systems—from autonomous vehicles and aerospace to healthcare and energy infrastructure—the stakes for safety, ethics, and reliability have never been higher. Welcome to 'Safety, Ethics, and Verification of AI in Engineering Systems,' a comprehensive course designed to equip engineers, data scientists, and system architects with the essential skills to build trustworthy AI applications.

As artificial intelligence becomes increasingly integrated into critical engineering systems—from autonomous vehicles and aerospace to healthcare and energy infrastructure—the stakes for safety, ethics, and reliability have never been higher. Welcome to 'Safety, Ethics, and Verification of AI in Engineering Systems,' a comprehensive course designed to equip engineers, data scientists, and system architects with the essential skills to build trustworthy AI applications. This course bridges the gap between cutting-edge AI development and rigorous engineering standards. You will explore the foundational principles of ethical and safety-critical AI, learning how to identify hazards, mitigate biases, and ensure human-centric governance. Dive deep into uncertainty quantification, risk-aware decision-making, and robustness against adversarial inputs and operational failures. Through practical modules, you will master Explainable AI (XAI) techniques to make black-box models transparent and accountable. Furthermore, the curriculum covers vital Verification and Validation (V&V) strategies and compliance with industry-leading standards such as ISO 26262, IEC 61508, and the NIST AI Risk Management Framework. By the end of this course, culminating in a real-world capstone scenario, you will possess the expertise to design, test, and deploy AI systems that are not only high-performing but fundamentally safe, ethical, and resilient.

Safety, Ethics, and Verification of AI in Engineering Systems

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            <p>The integration of artificial intelligence into complex engineering systems presents unprecedented opportunities for innovation, yet it simultaneously introduces profound challenges regarding safety, reliability, and ethical responsibility. As AI applications expand into high-stakes domains such as transportation, aerospace, healthcare, and critical infrastructure, the potential consequences of system failures escalate significantly. This course, Safety, Ethics, and Verification of AI in Engineering Systems, provides a rigorous examination of the methodologies required to design, evaluate, and deploy AI technologies that are not only highly capable but also fundamentally trustworthy. By addressing the intersection of technical performance and sociotechnical impact, the curriculum establishes a comprehensive framework for mitigating risks associated with autonomous and algorithmic decision-making.</p>

<p>Throughout this program, learners will systematically explore the lifecycle of responsible AI engineering. The curriculum begins with the foundational principles of safety-critical systems and ethical governance, progressing into advanced technical strategies such as uncertainty quantification, risk-aware decision-making, and adversarial robustness. Participants will examine the critical role of Explainable AI (XAI) in facilitating human-centered assurance and meaningful oversight. Furthermore, the course delves into rigorous verification and validation (V&V) practices, aligning testing strategies with industry standards and compliance frameworks. Ultimately, through integrated deployment methodologies and a comprehensive capstone scenario, professionals will acquire the analytical tools necessary to construct robust assurance cases and implement lifecycle governance for safety-relevant engineering applications.</p>
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