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Explainability (XAI) Gap Analysis: Autonomous Vehicles, Weapons Systems, Medical Diagnostics Fundamentals
Analyze explainability gaps in key AI domains with "Explainability (XAI) Gap Analysis: Autonomous Vehicles, Weapons Systems, Medical Diagnostics Fundamentals." This course addresses the need for transparent AI decision-making across critical applications. Cybersecurity is highlighted as a core concern since black-box systems may conceal malicious manipulations or systemic errors. Learn how explainability supports forensic analysis, threat detection, and regulatory compliance. In domains like autonomous weapons and diagnostics, explainability improves cyber defense by enabling human auditors to detect unauthorized behavior, ensuring secure and verifiable AI deployment.
Course Overview
None
Learning Objectives
- Understand the principles of Explainable AI (XAI) and its importance.
- Analyze the gap between human understanding and AI decision-making.
- Evaluate the application of XAI in autonomous vehicles, weapons systems, and medical diagnostics.
- Identify the challenges in implementing XAI for critical systems.
- Discuss the future trends and advancements in XAI research.
Framework Connections
The materials within this course focus on the NICE Framework Task, Knowledge, and Skill statements identified within the indicated NICE Framework component(s):
Competency Areas
Feedback
If you would like to provide feedback on this course, please e-mail the NICCS team at NICCS@mail.cisa.dhs.gov. Please keep in mind that NICCS does not own this course or accept payment for course entry. If you have questions related to the details of this course, such as cost, prerequisites, how to register, etc., please contact the course training provider directly. You can find course training provider contact information by following the link that says “Visit course page for more information...” on this page.