Transparent AI for Resource Allocation
Combining LLMs with Reinforcement Learning for Explainable Decision-Making
This research presents a novel Rule-Bottleneck Reinforcement Learning framework that combines the decision-making power of deep RL with the transparency of language models.
- Creates human-understandable policies for complex resource allocation problems
- Bridges the gap between effective AI decision-making and explainability
- Enables adaptable deployment alongside human decision-makers
- Particularly valuable for healthcare resource management decisions
For healthcare organizations, this approach offers transparent AI systems that can optimize resource allocation while providing clear explanations that medical professionals can understand, validate, and modify when necessary.