Call for Participation

While both industry and research communities focus substantial work on AI, the development of new AI technology and implementation of AI systems are two different challenges. Current AI solutions often undergo limited testing in controlled environments and their performance is difficult to replicate, verify, and validate. To improve reliable deployment of AI and enable trust and confidence in AI systems, implementers need access to leading practices, processes, tools, and frameworks.

Call for Participation now closed.

Our symposium will involve a mix of keynote and invited talks, breakout sessions, and panel discussions. We look forward to explorations of what AI engineering can and should entail.

Topics

We encourage participation on topics that explore pillars individually or at intersections. Examples of relevant submissions include (but are not limited to):

Beyond Accuracy: Enhanced Model Evaluation Metrics
Design for Human-Machine Teaming
Evaluating MLOps Pipelines and Tools
Budget Constraints in Adversarial Machine Learning
Broad and Wide Scalability Patterns for AI Systems
How to Tell if Your Dataset is Sufficient to Solve Your Problem
Maintaining Value Alignment in AI Systems Operations
Methods for Creating and Demonstrating Trust in AI Systems

Organizing Committee

Missy Cummings (Duke University)
Rachel Dzombak (CMU SEI)
Matthew Gaston (CMU SEI)
Karen Myers (SRI International)
William Streilein (MIT Lincoln Laboratory)

AI Engineering Symposium
AAAI Spring Symposium Series 2022