Documentation as Infrastructure for Accountable AI Systems

Jonathan King

Abstract

This perspective examines documentation artifacts as operational infrastructure for accountable AI. The organizing question is how model, data, deployment, and incident records can remain connected across a system lifecycle. Ten related scholarly sources are synthesized through a decision-centered framework spanning problem definition, mechanism, measurement, evaluation, implementation, and governance. The review does not invent experiments, pooled estimates, or unreported quantitative results. It instead evaluates the strength and transferability of the available evidence, with particular attention to publishing static transparency documents that are detached from engineering change. The resulting framework links technical or empirical performance to explicit use conditions and identifies tests that should precede wider adoption in organizations deploying learning systems across changing contexts.

Keywords

responsible AImodel cardsdatasheetsauditsaccountabilitydocumentation

References

Cobbe, J., Veale, M., & Singh, J. (2023). Understanding Accountability in Algorithmic Supply Chains. Scholarly publication. https://doi.org/10.31235/osf.io/p4sey DOI
Fehr, J., Citro, B., Malpani, R., Lippert, C., & Madai, V. I. (2024). A trustworthy AI reality-check: the lack of transparency of artificial intelligence products in healthcare. Frontiers in Digital Health, 6, 1267290. https://doi.org/10.3389/fdgth.2024.1267290 DOI
Hartmann, D., Pereira, J. R. L. D., Streitbörger, C., & Berendt, B. (2024). Addressing the regulatory gap: moving towards an EU AI audit ecosystem beyond the AI Act by including civil society. AI and Ethics, 5(4), 3617-3638. https://doi.org/10.1007/s43681-024-00595-3 DOI
Königstorfer, F., & Thalmann, S. (2022). AI Documentation: A path to accountability. Journal of Responsible Technology, 11, 100043. https://doi.org/10.1016/j.jrt.2022.100043 DOI
Liu, X., Glocker, B., McCradden, M. M., Ghassemi, M., Denniston, A. K., & Oakden-Rayner, L. (2022). The medical algorithmic audit. The Lancet Digital Health, 4(5), e384-e397. https://doi.org/10.1016/s2589-7500(22)00003-6 DOI
Lund, B., Orhan, Z., Mannuru, N. R., Bevara, R. V. K., Porter, B., Vinaih, M. K., & Bhaskara, P. (2025). Standards, frameworks, and legislation for artificial intelligence (AI) transparency. AI and Ethics, 5(4), 3639-3655. https://doi.org/10.1007/s43681-025-00661-4 DOI
Micheli, M., Hupont, I., Delipetrev, B., & Soler-Garrido, J. (2023). The landscape of data and AI documentation approaches in the European policy context. Ethics and Information Technology, 25(4). https://doi.org/10.1007/s10676-023-09725-7 DOI
Mylrea, M., & Robinson, N. (2023). Artificial Intelligence (AI) Trust Framework and Maturity Model: Applying an Entropy Lens to Improve Security, Privacy, and Ethical AI. Entropy, 25(10), 1429. https://doi.org/10.3390/e25101429 DOI
Naja, I., Markovic, M., Edwards, P., Pang, W., Cottrill, C., & Williams, R. (2022). Using Knowledge Graphs to Unlock Practical Collection, Integration, and Audit of AI Accountability Information. IEEE Access, 10, 74383-74411. https://doi.org/10.1109/access.2022.3188967 DOI
Sandu, I., Wiersma, M., & Manichand, D. (2022). Time to audit your AI algorithms. Maandblad Voor Accountancy en Bedrijfseconomie, 96(7/8), 253-265. https://doi.org/10.5117/mab.96.90108 DOI

Publication details

Journal: Machine Intelligence & Responsible Systems
Volume: 1 (2026)
Article number: mi20260002
License: CC BY 4.0

[1] Cobbe, J., Veale, M., & Singh, J. (2023). Understanding Accountability in Algorithmic Supply Chains. Scholarly publication. https://doi.org/10.31235/osf.io/p4sey DOI

[2] Fehr, J., Citro, B., Malpani, R., Lippert, C., & Madai, V. I. (2024). A trustworthy AI reality-check: the lack of transparency of artificial intelligence products in healthcare. Frontiers in Digital Health, 6, 1267290. https://doi.org/10.3389/fdgth.2024.1267290 DOI

[3] Hartmann, D., Pereira, J. R. L. D., Streitbörger, C., & Berendt, B. (2024). Addressing the regulatory gap: moving towards an EU AI audit ecosystem beyond the AI Act by including civil society. AI and Ethics, 5(4), 3617-3638. https://doi.org/10.1007/s43681-024-00595-3 DOI

[4] Königstorfer, F., & Thalmann, S. (2022). AI Documentation: A path to accountability. Journal of Responsible Technology, 11, 100043. https://doi.org/10.1016/j.jrt.2022.100043 DOI

[5] Liu, X., Glocker, B., McCradden, M. M., Ghassemi, M., Denniston, A. K., & Oakden-Rayner, L. (2022). The medical algorithmic audit. The Lancet Digital Health, 4(5), e384-e397. https://doi.org/10.1016/s2589-7500(22)00003-6 DOI

[6] Lund, B., Orhan, Z., Mannuru, N. R., Bevara, R. V. K., Porter, B., Vinaih, M. K., & Bhaskara, P. (2025). Standards, frameworks, and legislation for artificial intelligence (AI) transparency. AI and Ethics, 5(4), 3639-3655. https://doi.org/10.1007/s43681-025-00661-4 DOI

[7] Micheli, M., Hupont, I., Delipetrev, B., & Soler-Garrido, J. (2023). The landscape of data and AI documentation approaches in the European policy context. Ethics and Information Technology, 25(4). https://doi.org/10.1007/s10676-023-09725-7 DOI

[8] Mylrea, M., & Robinson, N. (2023). Artificial Intelligence (AI) Trust Framework and Maturity Model: Applying an Entropy Lens to Improve Security, Privacy, and Ethical AI. Entropy, 25(10), 1429. https://doi.org/10.3390/e25101429 DOI

[9] Naja, I., Markovic, M., Edwards, P., Pang, W., Cottrill, C., & Williams, R. (2022). Using Knowledge Graphs to Unlock Practical Collection, Integration, and Audit of AI Accountability Information. IEEE Access, 10, 74383-74411. https://doi.org/10.1109/access.2022.3188967 DOI

[10] Sandu, I., Wiersma, M., & Manichand, D. (2022). Time to audit your AI algorithms. Maandblad Voor Accountancy en Bedrijfseconomie, 96(7/8), 253-265. https://doi.org/10.5117/mab.96.90108 DOI