Provenance-First AI Scientists: Cryptographically Signed Evidence Graphs and Ethical Dashboards

TL;DR: Make every claim carry its receipts—code, data, and citations—locked with a signature so anyone can audit what the AI did and why. We’ll add an evidence ledger and an ethics dashboard to Kosmos, then test whether transparency boosts expert trust and reduces audit time without slowing discovery.

Research Question: Can cryptographically signed evidence graphs and structured ethics dashboards improve trust, accountability, and adoption of AI scientists while preserving performance?

Hypothesis: A provenance-first design that (i) signs every artifact (code cell, dataset slice, retrieval, figure) and (ii) summarizes ethical risks (data leakage, bias hotspots, authorship credit) will reduce expert audit time and increase trust ratings, with minimal runtime overhead.

Experiment Plan: - Setup:

• Evidence ledger: tamper-evident signing of all artifacts and edges in the world model; attach hashes and timestamps.
• Ethics dashboard: standardized reporting on data provenance, potential biases, sensitive content handling, and authorship/credit allocation.
• Governance hooks: configurable policies that can halt or flag steps violating ethical constraints (e.g., protected health info).
• Data/Materials:
  • Mixed-domain runs: materials science literature synthesis (low sensitivity) and clinical review tasks (higher sensitivity) to test governance range.
• Measurements:
  • Audit time and inter-rater agreement among independent scientists.
  • Trust and perceived integrity scores (surveys).
  • Performance overhead (wall-clock time) and discovery metrics.
• Expected Outcomes:
  • 20–40% reduction in audit time; increased trust ratings without loss in discovery yield.
  • Clearer authorship/accountability chains that address concerns raised in ethics reviews.

References: ['Shah, Z., Shahzad, M. H., Saleem, S., Taj, I., Amin, S., Almagharbeh, W. T., Muhammad, S. K., & Durvesh, S. (2025). Ethical Considerations in the Use of AI for Academic Research and Scientific Discovery: A Narrative Review. Insights-Journal of Life and Social Sciences.', 'Leslie, D. (2023). Does the sun rise for ChatGPT? Scientific discovery in the age of generative AI. AI and Ethics.', 'Eger, S., Cao, Y., D’Souza, J., Geiger, A., Greisinger, C., Gross, S., Hou, Y., Krenn, B., Lauscher, A., Li, Y., Lin, C., Moosavi, N., Zhao, W., & Miller, T. (2025). Transforming Science with Large Language Models: A Survey on AI-assisted Scientific Discovery, Experimentation, Content Generation, and Evaluation. arXiv.org.', 'Sai, S., Gaur, A., Sai, R., Chamola, V., Guizani, M., & Rodrigues, J. (2024). Generative AI for Transformative Healthcare: A Comprehensive Study of Emerging Models, Applications, Case Studies, and Limitations. IEEE Access.', 'Mitchener, L., Yiu, A., Chang, B., Bourdenx, M., Nadolski, T., Sulovari, A., et al. (2025). Kosmos: An AI Scientist for Autonomous Discovery. Preprint.']