American Intelligence Labs / Research desk
Research agenda.
AIL studies how operational information can become more structured, reviewable, and useful without hiding uncertainty or displacing human responsibility.
From the research desk
Newest working notes.
Methods and definitions published with their question, status, sources, and limits. These are working documents, not peer-reviewed findings.
- Method NoteAIL-MN-001
Designing a structured location inquiry
A working method for defining location outreach, separating reported facts from inference, and preserving human review before an operational decision.
- Working NoteAIL-WN-001
What counts as reviewable operational evidence
A working definition of the record needed to inspect how an AI-assisted operational finding was sourced, transformed, reviewed, and used.
Current agenda
Three questions under study.
Each program begins with a defined question and method. Transition requires evidence and controls suited to the intended use—not a persuasive demonstration.
- RESEARCH PROGRAM
Probabilistic Forecasting & Market Simulation
AIL Sports Intelligence
Explore the Research ProgramQuestion
Can a probabilistic model remain calibrated as public expectations, available information, and market prices change?
Proposed method
Compare explicit probability ranges with observable prices and outcomes, run stated scenarios, and track calibration and error over time.
Transition condition
Advance only after data provenance, evaluation rules, legal boundaries, and the intended decision use are defined for a specific research context.
Current limit
Research and decision-support only. No wagering action, guaranteed prediction accuracy, winnings, or profit claims.
- RESEARCH PROGRAM
Structured Recruiting Decision Support
AIL Talent Intelligence
Explore the Research ProgramQuestion
Can job-relevant evidence be organized more consistently without displacing accountable human judgment?
Proposed method
Structure role requirements, interview prompts, work samples, and reviewer observations, then examine the completeness and consistency of the review record.
Transition condition
Advance only with job-relevance review, bias and accessibility assessment, excluded-use safeguards, and a named human decision owner.
Current limit
Human decision support only. No personality diagnosis, protected-trait analysis, autonomous hiring decision, or sole-basis employment recommendation.
- RESEARCH PROGRAM
Readiness, Decision Intelligence & Secure Knowledge
AIL Mission Systems
Explore Mission SystemsQuestion
How can decision-makers retrieve grounded, permissioned knowledge and rehearse scenarios without obscuring uncertainty or authority?
Proposed method
Prototype source-grounded retrieval, permission controls, scenario exercises, uncertainty records, and explicit human review paths within a defined research scope.
Transition condition
Advance only after data authority, security boundaries, evaluation criteria, and decision ownership are specified for the research context.
Current limit
Research program. No government or military affiliation, contract, endorsement, certification, procurement status, or operational deployment is implied.
Review standard
Make the reasoning inspectable.
Responsible AI principlesDefine the decision.
Name the operating question, intended user, permitted information, and decision owner before evaluating a system.
Preserve the record.
Keep sources, transformations, uncertainty, exceptions, and material limits available for review.
Earn the transition.
Move beyond research only when the evidence, safeguards, authority, and use conditions are defined for that context.
A question worth examining
Start with the decision and its limits.
Tell us what decision matters, what information is permitted, who is responsible, and what would count as credible evidence.
Discuss a research question