PB-OEL: A Performance-Bounded Online Ensemble Learning Framework With Mixed Feedback for Real-Time Safety Assessment
arXiv:2503.15581v2 Announce Type: replace Abstract: Real-time safety assessment is critical for ensuring the reliable operation of complex dynamic systems. However, obtaining full safety labels in real time is often prohibitively expensive, resulting in a challenging mixed-feedback scenario dominated by partial feedback, especially under concept drift. Furthermore, existing online ensemble methods typically rely on heuristic weight allocation, lacking provable performance guarantees under such limited-feedback conditions. To address these challenges, we propose PB-OEL, a performance-bounded on
The increasing complexity and autonomy of AI systems necessitate robust real-time safety assessment, especially with the prevalence of partial feedback and concept drift in dynamic environments.
This development offers a method for reliable real-time safety assessment in complex AI systems, crucial for deployment in critical applications where safety guarantees are paramount.
The explicit focus on provable performance guarantees under limited-feedback conditions differentiates this approach from existing heuristic methods, potentially making online ensemble learning more trustworthy for real-time safety-critical scenarios.
- · AI developers in safety-critical domains
- · Robotics and autonomous systems manufacturers
- · Regulators and certification bodies
- · Insurance providers
- · Developers relying on heuristic safety methods
- · Companies with unreliable AI systems
Increased adoption of AI in high-stakes environments due to enhanced safety mechanisms.
Development of standardized safety assessment protocols based on performance-bounded methodologies.
Enhanced public trust in autonomous AI systems, accelerating their integration into daily life and critical infrastructure.
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