会议信息

TRUST 2027: ACM Conference on Trustworthy and Responsible AI and Computing Systems

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截稿日期:
2026-10-24 还有 15 天
通知日期:
2026-12-31
会议日期:
2027-03-07
会议地点:
Washington DC, USA
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TRUST
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会伴指数 (CP-I)

44.8 / 100
全站第 2,967 名 / 共 5,694 个会议 · 前 53%
证据有限：这个会议不在 CCF / ICORE / QUALIS 任何一份榜单里，也没有录用率数据，因此分数的大部分回落到了中性基准。
学术认可 (35%) 无数据 —— 按中性基准 50 分计入 —
投稿选择性 (20%) 无数据 —— 按中性基准 50 分计入 —
会议传承 (20%) 无数据 —— 按中性基准 50 分计入 —
社区关注 (10%)
20
资料公开度 (15%)
35

用到的输入: 在会伴关注它的研究者：1 人 · 过去 24 个月打开过本页的研究者：5 人

公开资料里还缺: 历年录用率 (+4.5) · 历届信息 (+3.0) · 最佳论文记录 (+2.3)
主办方认领本会议后，可直接在这一页补上；分数每晚重算。如何提升这个分数

置信度 25% —— 分数中有多大比例来自实际观测到的数据，而不是中性基准。 这个分数是怎么算出来的 · 查看完整榜单 · 算法版本 1.1 · 算于 2026-10-09

征稿

TRUST 2027 (ACM Conference on Trustworthy and Responsible AI and Computing Systems) is an academic conference held in Washington DC, USA on 2027-03-07. The paper submission deadline is 2026-10-24. Acceptance notifications are sent on 2026-12-31.

ACM TRUST 2027 welcomes original, unpublished research contributions that advance the design, verification, deployment, evaluation, and governance of trustworthy and responsible AI and computing systems. Scope Contributions may include new theories, algorithms, architectures, systems, benchmarks, evaluation frameworks, certification approaches, or deployment-oriented studies related to trustworthy and responsible AI and computing systems. Topics of Interest TRUST 2027 welcomes contributions across the following research areas. P1 Foundations of Trustworthy AI Formal verification of AI and ML systems Robustness, generalization, and adversarial resilience Interpretability and explainability foundations Uncertainty quantification and reliability metrics Causal reasoning and trustworthy inference Neuro-symbolic and hybrid AI approaches P2 Secure and Resilient AI Systems Adversarial machine learning Data poisoning and model backdoor defenses Secure model deployment pipelines Privacy-preserving AI and federated learning AI supply chain security Runtime monitoring and anomaly detection P3 Trustworthy AI in Systems and Infrastructure AI in cyber-physical systems Edge AI and trustworthy IoT Cloud and edge orchestration for safe AI AI reliability in distributed systems Trustworthy autonomous systems AI lifecycle management and MLOps assurance P4 Responsible AI, Governance and Compliance Fairness, bias mitigation, and accountability Transparency and auditability AI risk management frameworks Standards and certification of AI systems Ethical system design methodologies Benchmarking responsible AI practices P5 Evaluation, Benchmarking and Assurance Trustworthiness benchmarks Stress-testing frameworks Reproducibility and replicability in AI research Dataset integrity and data governance Risk assessment methodologies System-level validation and verification pipelines P6 Domain-Specific Trustworthy Applications Healthcare AI safety Smart grid and critical infrastructure Industrial AI and cyber-physical systems Financial AI risk control Public-sector AI systems Climate and sustainability systems
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CCF全称影响因子出版商ISSN
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