- Available as an on-demand course for a limited time until 24 May 2025.
- Navigate the intersection of AI, ethics, policy, and societal impact with this course tailored to the unique challenges of AI and aerospace applications.
- All students will receive an AIAA Certificate of Completion at the end of the course.
OVERVIEW
This is the second in a series of AIAA Artificial Intelligence 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.
Participants in these courses will gain a solid foundation in AI fundamentals, learn how to architect AI systems, understand the principles of systems engineering as they apply to AI, understand and evaluate ethical and societal implications, and manage AI risk. Graduates will be well-prepared to develop and manage complex systems with embedded AI, including identifying unique requirements, testing, and certifying these systems, and maintaining safe performance levels. This is particularly crucial for safety-critical systems, such as self-flying vehicles and drone delivery, where rigorous testing, evaluation, monitoring, and maintenance is paramount.
This course will use Responsible AI (RAI) to delve into pressing ethical, societal, and policy issues, such as transparency, trust, 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 AI governance for the aerospace sector.
- Understand Core Responsible AI Principles: Gain a thorough understanding of the foundational principles of Responsible AI, including transparency, fairness, and accountability.
- Understand RAI Frameworks: Explore various frameworks and methodologies for implementing Responsible AI, tailored to address ethical and regulatory considerations.
- Understand how Responsible AI intersects with public policy: Explore its specific applications and challenges within the aerospace sector.
- Use Responsible AI to Delve into Pressing Ethical, Societal, and Policy Issues: Analyze critical ethical, societal, and policy issues through the lens of Responsible AI, focusing on how these issues impact technology and its deployment.
- Apply RAI to Aerospace Use Cases: Apply Responsible AI principles to real-world aerospace scenarios, addressing practical challenges and ensuring ethical and effective AI integration.
Professionals tasked with driving the safe and effective integration of AI within their organizations, who are eager to enhance their expertise in the design, testing, and deployment of cutting-edge AI-based aerospace technologies.
CERTIFICATE: Receive an AIAA Course Completion Certificate upon viewing all course recordings. Please contact Lisa Le for a certificate.
COURSE FEES (Sign-In to Register)
- AIAA Member Price: $495 USD
- Non-Member Price: $695 USD
- AIAA Student Member Price: $295 USD
LEARNING TRACK: Responsible AI in Aerospace
- While courses can be taken on their own, students can bundle all four courses in the Responsible AI in Aerospace Learning Track with significant discounts.
- Students that attend all 4 courses will receive an additional AIAA Certificate of Completion.
------------------------------------------------------------------------------------------------------------OUTLINE- Introduction—Applied Ethics and AI: review and examination of how values and principles can be used to guide the development and application of AI systems for societal benefit.
- Responsible Innovation and Responsible AI (RAI): focuses on fostering innovation while ensuring that AI technologies are ethically sound, socially beneficial, and align with human values.
- RAI Principles and Frameworks: learn how RAI principles and frameworks provide a foundation for the ethical design, development, and governance of AI systems across industries.
- RAI Principles, Frameworks, and Guidelines: Explore guidelines based on RAI principles and frameworks and how they help organizations create AI systems that prioritize ethical considerations, accountability, and societal good.
- RAI Principles, Frameworks, and Guidelines Case Study: Review and analyze an aerospace case study, focusing on the use of AI in aircraft design and safety, and how RAI principles frameworks and guidelines intersect with key features of the case.
- Operationalizing RAI: Methods and Tools: learn how practical methods and tools are used to integrate RAI into the everyday design, development, and deployment of AI systems in aerospace.
- Fairness: this session explores how fairness plays a key role in ensuring that algorithms are designed to produce equitable outcomes for all individuals and groups, avoiding bias and discrimination.
- Transparency and Trust: explore how transparency in AI can foster trust in aerospace by making the decision-making processes of AI systems understandable and accessible to users and stakeholders.
- Accountability: this session focuses on holding developers, organizations, and systems responsible for the outcomes of AI decisions and ensuring mechanisms for redress.
- Safety and Security: focuses on preventing harm from AI systems by ensuring they operate reliably, securely, and with proper safeguards.
Dr. Jesse KirkpatrickJesse Kirkpatrick is a Research Associate Professor and the co-director of the Mason Autonomy and Robotics Center at George Mason University. Jesse is also an
International Security Fellow at New America and serves as a consultant for numerous organizations, including some of the world’s largest technology
companies. Dr. Kirkpatrick’s research and teaching focuses on responsible innovation, with an emphasis on Responsible AI. He has received numerous honors
and awards and is an official “Mad Scientist” for the U.S. Army.CLASSROOM HOURS / CEUs: 8 classroom hours / 0.8 CEU/PDH
Contact: Please contact Lisa Le or Customer Service if you have any questions about the course or group discounts.
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