arXiv:2410.14573v2 Announce Type: replace Abstract: Optimizing costly black-box functions within a constrained evaluation budget presents significant challenges in many real-world applications. Surrogate Optimization (SO) is a common resolution, yet its proprietary nature introduced by the complexity of surrogate models and the sampling core (e.g., acquisition functions) often leads to a lack of explainability and transparency. While existing literature has primarily concentrated on enhancing convergence to global optima, the practical interpretation of newly proposed strategies remains undere
The increasing complexity and opacity of AI models, particularly in black-box optimization, necessitate a focus on explainability to foster trust and broader adoption.
A strategic reader should care about explainable AI because it underpins the reliability, ethical deployment, and regulatory acceptance of advanced AI systems in critical applications.
This research highlights a shift in focus from mere performance optimization to the equally crucial aspect of understanding and interpreting AI decisions, which could influence future AI development paradigms.
- · AI developers focused on transparency
- · Industries with high regulatory oversight
- · Ethical AI frameworks
- · Users of complex AI systems
- · Proprietary black-box AI solution providers
- · AI systems lacking inherent interpretability
- · Developers prioritizing speed over transparency
Increased demand for explainable AI tools and methodologies across various applications.
New industry standards and regulatory requirements for AI transparency in sensitive domains.
A potential slowing of rapid AI deployment in certain sectors as explainability becomes a prerequisite for operational approval.
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Read at arXiv cs.LG