Respirator Selection Logic (RSL) Copilot
Description
Workers rely on NIOSH Approved respirators to protect them from inhaling high-consequence particulate, gas, and vapor hazards. Some examples of these respiratory hazards include: • Wildfire, structural, or surgical smoke • Mold during post-flood remediation efforts • Infectious diseases such as tuberculosis • Chemicals used to clean or disinfect • Particles liberated when cutting rock in industries such as construction and mining Selecting the correct respirator to protect workers requires knowledge of the hazard or hazards present, the job task, and the environment. NIOSH’s Respirator Selection Logic (RSL) is a state-of-the-art tool designed to guide the selection of appropriate respiratory protection devices based on specific workplace hazards and conditions. The RSL requires users to enter detailed, task- and environment-specific information at multiple decision points to execute its logic correctly.
Detailed example
Upon completion of this project, users of the RSL will be able to: • Receive real-time guidance on what information is required for respirator selection and why it matters. • Provide input in natural language rather than navigating technical documents or forms manually. • Understand and apply the RSL more effectively, leading to fewer errors in respirator selection, improved compliance, and stronger respiratory protection outcomes. • Use Ally as a decision support tool—not a decision maker—to identify and clarify required inputs, understand the rationale behind each RSL step, and access authoritative guidance. The Copilot will always keep the user in the loop, helping them apply judgment while ensuring traceability to official sources like NIOSH and OSHA. This project will demonstrate how AI can support complex public health decision tools like the RSL while maintaining user accountability, transparency, and regulatory defensibility.
AI / analytics pattern
Natural Language Processing: AI that processes, interprets, and shares information in human language.
Automation level / stage
a) Pre-deployment – The use case is in a development or acquisition status.
Expected benefit
No current AI tool operationalizes the RSL while addressing the challenge of gathering and validating highly specific input information required at each decision point. The absence of such assistance leads to errors in respirator selection that can cause hazardous exposures, regulatory violations, and adverse health outcomes. Developing Ally to close this gap will improve the effectiveness of respiratory protection programs by ensuring that users supply accurate, relevant data to the RSL, thereby enhancing the quality and traceability of respiratory protection decisions aligned with established federal guidance.
Controls / human review
ATO: Not reported; PIA: Not published