10/18/2021 (Anywhere on Earth) Paper Submission Deadline
11/22/2021 Acceptance Notification
12/30/2021 Camera-ready Papers Due
3/22/2022 − 3/24/2022 Conference
Call for Papers
ICAA seeks contributions on all aspects of AI safety, security, and privacy in autonomous systems. Papers that encourage the discussion and exchange of experimental and theoretical results, novel designs, and works in progress are preferred. Topics of interest include (but are not limited to):
Autonomous System and AI Safety
Detecting dataset anomalies that lead to unsafe AI decisions Evaluating safety of autonomous systems according to their potential risks and vulnerabilities Resilient, explainable deep learning, and interpretable machine learning Verification, testing and acceptance of machine learning models and autonomous systems Autonomic computing for autonomous system safety Standards, ethics, and policies for autonomy and AI safety Ethics of autonomous system behaviors, algorithms and implementations Safety and assurance of human-autonomy teaming
Security and Privacy of Autonomous Systems and AI
Detecting dataset anomalies that lead to autonomous system security and privacy violations Differential privacy and privacy-preserving learning and generative models Adversarial attacks on AI and autonomy, and defenses against adversarial attacks Improving resiliency of AI and autonomous system methods and algorithms to various forms of attacks Engineering trusted autonomous system and AI software architectures
[External Event] 2022 IEEE Conference on Assured Autonomy (ICAA)
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Important Dates
10/18/2021 (Anywhere on Earth) Paper Submission Deadline
11/22/2021 Acceptance Notification
12/30/2021 Camera-ready Papers Due
3/22/2022 − 3/24/2022 Conference
Call for Papers
ICAA seeks contributions on all aspects of AI safety, security, and privacy in autonomous systems. Papers that encourage the discussion and exchange of experimental and theoretical results, novel designs, and works in progress are preferred. Topics of interest include (but are not limited to):
Autonomous System and AI Safety
Detecting dataset anomalies that lead to unsafe AI decisions
Evaluating safety of autonomous systems according to their potential risks and vulnerabilities
Resilient, explainable deep learning, and interpretable machine learning
Verification, testing and acceptance of machine learning models and autonomous systems
Autonomic computing for autonomous system safety
Standards, ethics, and policies for autonomy and AI safety
Ethics of autonomous system behaviors, algorithms and implementations
Safety and assurance of human-autonomy teaming
Security and Privacy of Autonomous Systems and AI
Detecting dataset anomalies that lead to autonomous system security and privacy violations
Differential privacy and privacy-preserving learning and generative models
Adversarial attacks on AI and autonomy, and defenses against adversarial attacks
Improving resiliency of AI and autonomous system methods and algorithms to various forms of attacks
Engineering trusted autonomous system and AI software architectures