# Ethics, Limitations, and Responsible Use

This project is an experimental research framework for studying adaptive meta-learning and model-level adaptation strategies. The purpose of this document is to (1) clarify limitations and (2) provide guidance for responsible use.

Limitations
- The codebase implements research probes such as introspection and self-monitoring modules. These are algorithmic components and should not be interpreted as evidence of consciousness, sentience, or independent agency.
- Experimental results are sensitive to dataset selection, hyperparameters, random seeds, and compute scale. Reproducibility requires publishing configs, seeds, and environment information.
- Any performance numbers reported in documentation must be accompanied by reproducible scripts, datasets (or clear data generation instructions), and random-seed statistics.

Responsible use
- Do not claim biological-like consciousness or human-equivalent mental states without rigorous interdisciplinary validation (philosophy, cognitive science, neuroscience) and independent replication.
- Consider potential misuse: models that self-modify can alter behavior unpredictably. Use safety checks, human oversight, and conservative update rules in deployment.
- For public releases that include model checkpoints, ensure data licensing and privacy constraints are satisfied.

Recommendations for publication
- Provide a clear Reproducibility Appendix: commit SHA, exact `requirements.txt`, config files, seed values, and small-scale scripts that reproduce key plots or tables.
- Provide baseline comparisons and statistical summaries (mean ± std over multiple seeds) rather than single-run numbers.
- Include an explicit Ethics & Limitations section in any paper or public release.

If you would like, I can draft a short `ETHICS.md` section for the paper skeleton and insert a recommended Limitations paragraph into `README.md` and `FRAMEWORK_README.md`.
