Skip to content
Apart Research
Apart Research
Contact us
Sprint projectMar 23, 2026Richmond, UT

Structured Ethical Justification Protocol (SEJP): Ordained-Ethics Enforcement and Reasoning-Alignment Monitoring for AI Systems

Shaker Funkhouser

Submitted to AI Control Hackathon 2026. Sprint projects are early-stage work by participants, not Apart Research publications.

Read the report

Report: Structured Ethical Justification Protocol (SEJP): Ordained-Ethics Enforcement and Reasoning-Alignment Monitoring for AI Systems

Open in a new tab (opens in new tab)
Code (opens in new tab)More on github.com (opens in new tab)
Share

We apply invariant moral calculations to detect and block scheming agents.

Reviews

Judging this Sprint?

Review this project

Your public critique appears on this page without your name. Your private critique is not published; only the Apart team reads it. If you agree below, we share your review with grantmaking.ai (opens in new tab) and the Transformative AI Fund so strong projects can be funded.

Not shown on this page.

Shown on this page, without your name.

Only the Apart team reads this, and funders if you agree below.

Share my name publicly on grantmaking.ai *
Share my private critique with funders *

How much would this matter for AI safety if it worked? How innovative is it? For scores of 4-5: is this actually new to the field, or replicating recent work?

Scoring guide
  1. 1Negligible. No clear problem addressed, or no meaningful novelty.
  2. 2Limited. Addresses a real problem but with a generic or well-trodden approach. Incremental at best.
  3. 3Moderate. Clear problem with a reasonable approach; some novelty in framing or method beyond routine application of existing tools.
  4. 4Significant. Important problem with an original approach, or identifies a neglected problem area. A valuable contribution others could build on.
  5. 5Exceptional. Tackles a critical AI safety problem with a genuinely novel approach, or opens a new research direction. Clear theory of change. You'd be excited to share this with researchers in the area.

How sound are methodology, implementation, and findings?

Scoring guide
  1. 1Seriously flawed. Methodology broken, results uninterpretable, or implementation doesn't work.
  2. 2Weak. Approach has significant gaps: missing validation, flawed experimental design, or incomplete implementation.
  3. 3Competent. Technically solid given the short duration. Methodology makes sense, results are interpretable, limitations acknowledged, work builds toward clear conclusions.
  4. 4Strong. Thorough methodology with convincing validation. Results clearly support conclusions. Immediately useful for future work.
  5. 5Exceptional. Ambitious scope executed rigorously. Surprising findings, novel methods, or unusually robust validation.

How clearly are work, findings, and impact potential communicated?

Scoring guide
  1. 1Incomprehensible. Cannot determine what the project is actually claiming or doing.
  2. 2Hard to follow. Key information buried, missing, or diluted by excessive length. Significant effort to extract main points.
  3. 3Clear enough. Can understand the problem, approach, and results without undue effort. Core content clearly present: problem, method, findings, limitations.
  4. 4Well presented. Easy to follow, well-structured, appropriate level of detail. Target audience would get it quickly.
  5. 5Exceptionally clear. A pleasure to read. Complex ideas made accessible. Could serve as a model for how to present this type of work.

  1. There's a disconnect between the high-level language of "ethics" and "morals" employed here and the mechanical implementation of verifiable outcomes. That said, measuring self-consistency of professed justifications in scenarios that lend themselves to semi-objective evaluations of this form is an interesting approach and deserves further exploration. It would also be interesting to explore if and how well the retry exhaustion pattern generalizes.

  2. They constrain the action space so only the morally correct action is available instead of monitoring. No LLM inference needed. Audit component checks if stated reasoning matches the math. Interesting approach, retry exhaustion finding is useful. But only works when actions are finite and enumerable which rules out most of what agentic AI actually does. Guard claiming 100% block rate when it only allows one action isn't really a result. Small samples everywhere.

Cite this project

@misc{funkhouser2026structured,
  title = {{Structured Ethical Justification Protocol (SEJP): Ordained-Ethics Enforcement and Reasoning-Alignment Monitoring for AI Systems}},
  author = {Shaker Funkhouser},
  year = {2026},
  month = mar,
  note = {Submitted to AI Control Hackathon 2026, an Apart Research Sprint},
  howpublished = {\url{https://apartresearch.com/sprints/projects/structured-ethical-justification-protocol-sejp-ordainedethics-enforcement-and-reasoningalignment-monitoring-for-ai-systems-4s34}},
  url = {https://apartresearch.com/sprints/projects/structured-ethical-justification-protocol-sejp-ordainedethics-enforcement-and-reasoningalignment-monitoring-for-ai-systems-4s34}
}

More from AI Control Hackathon 2026

All 125 entriesBrowse all projects

Build something like this at the next Sprint

AI Collusion Research Sprint · Oct 23 - 25, 2026