AI Design and Deployment Risks for Aerospace Applications

AI Design and Deployment Risks for Aerospace Applications


  • Deepen your understanding of aerospace autonomy and AI risk management with this fourth AIAA course in the Responsible AI series. It focuses on measuring, assessing, and mitigating the inherent risks in AI systems, from safety and security to governance and legal interventions.
  • All students will receive an AIAA Certificate of Completion at the end of the course.

OVERVIEW

This is the third in a series of AIAA Artificial Intelligence (AI) short courses focusing on Responsible AI. These courses are tailored to equip aerospace professionals with the essential knowledge, skills, and analytical abilities to tackle the challenges of responsibly designing and deploying AI-integrated systems. As AI becomes increasingly embedded in aerospace, it presents significant opportunities for efficiency, cost reduction, and safety enhancement. However, recognizing and mitigating the associated risks is essential to ensure the safety, reliability, and ethical integrity of these technologies.

This course will explore the fundamental issues that underpin risk inherent in aerospace systems that utilize AI. Students will learn how to measure these risks, assess the impacts and harms that could result from AI, and formulate plans for managing risks including testing, maintenance, governance and legal interventions. Topics will include AI robustness, generalizability, validity, reliability, safety, and security.
These topics will be examined through specific aerospace use cases and current events, providing a grounded understanding of the challenges and considerations in autonomy and AI for the aerospace sector. This course will cover examples and use cases of AI applications in aerospace such asunderstanding AI risk fundamentals, measuring AI risks, predicting and assessing AI impact, and formulating risk management plans.

AUDIENCE
This course is designed for professionals tasked with driving the safe and effective integration of AI within their organizations, including the design, testing, and deployment of cutting-edge AI-based aerospace technologies.

COURSE INFORMATION
Type of Course: Instructor-Led Short Course
Course Level: Fundamentals
Course Length: 1 day
AIAA CEU’s available: Yes

Frequently Asked Questions

Outline
  • AI, Autonomy & Human Decision-making: AI and autonomy are defined, along with explanations of how symbolic and connectionist AI function (including generative AI). These will be compared and contrasted to human decision-making.
  • Risk & Systems: Risk and systems engineering fundamentals will be reviewed, with a discussion about how AI has changed these approaches.
  • Hazards & Risks: Hazards unique to AI in aerospace systems will be covered as well as how hazard analysis techniques should be updated.
  • Uncertainty & Perceived Risk: How, where and why uncertainty influences AI-embedded systems will be discussed, as well as the risk that various sources of uncertainty introduces into aerospace systems.
  • AI & Safety: How safety models have changed with the introduction AI in aerospace applications will be discussed, along with AI safety management techniques.
  • Human-AI Interaction: This module will discuss to pros and cons of human interactions with AI in both the design and use of aerospace systems.
  • Explainable/Interpretable/Trustworthy AI: Definitions will be discussed, along with AI use cases to illustrate how these different concepts can be applied in aerospace applications.
  • Testing & AI: How and why AI affects both developmental and operational testing will be discussed, along with unique AI considerations like software upgrades and certification concerns.
  • AI Bias Management: How to manage the bias that is inherent in all AI systems will be discussed across the aerospace systems engineering lifecycle.
  • AI, Cybersecurity & Risk: This module will cover what unique changes AI brings to aerospace cybersecurity and current mitigation strategies.
Materials
Instructors

Professor Mary (Missy) Cummings received her B.S. in Mathematics from the US Naval Academy in 1988, M.S. in Space Systems Engineering from the Naval Postgraduate School in 1994, and Ph.D. in Systems Engineering from the University of Virginia in 2004. A naval pilot from 1988-1999, she was one of the U.S. Navy’s first female fighter pilots. She is a Professor in the George Mason University College of Engineering and Computing, and directs the Mason Responsible AI program as well as the Mason Autonomy and Robotics Center. She is an American Institute of Aeronautics and Astronautics and Royal Aeronautical Society Fellow, and is a Commissioner for the Global Commission on Responsible Artificial Intelligence in the Military Domain.

 

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