Artificial Intelligence Engineering
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My AI System Works…But Is It Safe to Use?
This blog post introduce System Theoretic Process Analysis (STPA), a hazard analysis technique uniquely suitable for dealing with the complexity of AI systems.
Read More•By David Schulker, Matthew Walsh, Emil Mathew
In Artificial Intelligence Engineering
Artificial Intelligence in National Security: Acquisition and Integration
This blog post highlights practitioner insights from a recent AI Acquisition workshop, including challenges in differentiating AI systems, guidance on when to use AI, and matching AI tools to mission …
Read More•By Paige Rishel, Carol J. Smith, Brigid O'Hearn, Rita C. Creel
In Artificial Intelligence Engineering
Amplifying AI Readiness in the DoD Workforce
The SEI recently partnered with the Department of the Air Force Chief Data and AI Office to develop a strategy to identify and assess hidden workforce talent for data and …
Read More•By Eric Keylor, Robert W. Beveridge, Jonathan Frederick
In Artificial Intelligence Engineering
Out of Distribution Detection: Knowing When AI Doesn't Know
How do we know when an AI system is operating outside its intended knowledge boundaries?
Read More•By Eric Heim, Cole Frank
In Artificial Intelligence Engineering
10 Things Organizations Should Know About AI Workforce Development
This post outlines 10 recommendations developed in response to work with our mission partners in the Department of Defense.
Read More•By Jonathan Frederick, Dominic A. Ross, Eric Keylor, Cole Frank, Intae Nam
In Artificial Intelligence Engineering
DataOps: Towards More Reliable Machine Learning Systems
Decisions based on ML models can have significant consequences, and managing the raw material—data—in ML systems is a challenge. This post explains DataOps, an area that focuses on the management …
Read More•By Daniel DeCapria
In Artificial Intelligence Engineering
Evaluating LLMs for Text Summarization: An Introduction
Deploying LLMs without human supervision and evaluation can lead to significant errors. This post outlines the fundamentals of LLM evaluation for text summarization in high-stakes applications.
Read More•By Shannon Gallagher, Swati Rallapalli, Tyler Brooks
In Artificial Intelligence Engineering
The Essential Role of AISIRT in Flaw and Vulnerability Management
The SEI established the first Artificial Intelligence Security Incident Response Team (AISIRT) in 2023. This post discusses the role of AISIRT in coordinating flaws and vulnerabilities in AI systems.
Read More•By Lauren McIlvenny, Vijay S. Sarvepalli
In Artificial Intelligence Engineering
Enhancing Machine Learning Assurance with Portend
This post introduces Portend, a new open source toolset that simulates data drift in machine learning models and identifies the proper metrics to detect drift in production environments.
Read More•By Jeffrey Hansen, Sebastián Echeverría, Lena Pons, Gabriel Moreno, Grace Lewis, Lihan Zhan
In Artificial Intelligence Engineering
Protecting AI from the Outside In: The Case for Coordinated Vulnerability Disclosure
This post highlights lessons learned from applying the coordinated vulnerability disclosure (CVD) process to reported vulnerabilities in AI and ML systems.
Read More•By Allen D. Householder, Vijay S. Sarvepalli, Jeff Havrilla, Matt Churilla, Lena Pons, Shing-hon Lau, Nathan M. VanHoudnos, Andrew Kompanek, Lauren McIlvenny
In Securing AI
