Conference Information

ICAA'' 2022: IEEE Conference on Assured Autonomy

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Submission Date:
2021-11-22 Closed
Notification Date:
2021-12-20
Conference Date:
2022-03-22
Location:
Online
Viewed: 12135   Tracked: 0   Attend: 0
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ICAA''
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Conference Partner Index (CP-I)

42.1 / 100
Ranked #4,189 of 5,705 conferences · Top 74%
Limited evidence: this conference is not listed in CCF / ICORE / QUALIS and has no acceptance-rate data on file, so most of the score falls back to the neutral baseline.
Academic recognition (35%) No data - scored at the neutral baseline of 50 —
Submission selectivity (20%) No data - scored at the neutral baseline of 50 —
Editions held (20%) No data - scored at the neutral baseline of 50 —
Community attention (10%)
8
Public record completeness (15%)
25

Inputs used: Researchers who opened this page in the past 24 months: 2

Missing from the public record: Historical acceptance rates (+4.5) · Past editions (+3.0) · Best-paper records (+2.3)
Organizers can add these from this page after claiming the conference; scores are recomputed nightly. How to raise this score

Confidence 25% - the share of the score backed by observed data rather than the neutral baseline. How this score is calculated · Browse the ranking · Algorithm version 1.2 · Computed 2026-10-16

Call For Papers

ICAA'' 2022 (IEEE Conference on Assured Autonomy) is an academic conference held in Online on 2022-03-22. The paper submission deadline is 2021-11-22 (extended). Acceptance notifications are sent on 2021-12-20.

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
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