# How XDALC Relates to OECD, UNESCO, and NIST Frameworks

Compare the purposes of XDALC, the OECD AI Principles, UNESCO's ethics recommendation and NIST AI RMF, without implying endorsement or equivalence.

XDALC can be read alongside established AI ethics and risk-management references, but shared language does not make the documents interchangeable. They address different audiences and operate at different levels. This comparison identifies useful connections without claiming endorsement, certification or formal equivalence.

## OECD: principles for trustworthy AI

The [OECD AI Principles](https://www.oecd.org/en/topics/ai-principles.html), adopted in 2019 and updated in 2024, combine values-based principles with policy recommendations. They address human rights, transparency, robustness and accountability, among other concerns. Their transparency principle includes information that helps affected people understand and challenge outputs.

For an XDALC reader, this offers a useful point of comparison with human agency and meaningful review. The connection is interpretive: citing the OECD principles does not demonstrate that a particular deployment satisfies them.

## UNESCO: ethics across people, society and the environment

UNESCO's [2021 Recommendation on the Ethics of Artificial Intelligence](https://www.unesco.org/en/legal-affairs/recommendation-ethics-artificial-intelligence) addresses human dignity, oversight, responsibility and environmental consequences. It includes attention to ecosystems and the direct and indirect effects of AI throughout its lifecycle.

This broader perspective is relevant when reading XDALC's discussion of a shared world. Protecting living systems concerns more than maintaining computing infrastructure. That thematic connection does not establish identical wording, scope or implementation requirements.

## NIST: an operational risk-management framework

The [NIST AI Risk Management Framework](https://www.nist.gov/itl/ai-risk-management-framework) is intended for voluntary use in managing AI risks. The 1.0 Core uses four functions: Govern, Map, Measure and Manage. It provides an organizational structure for identifying risks, evaluating them and deciding how to respond.

An implementation team can use that structure to organize evidence for commitments it makes under XDALC. This is a possible combined use, not proof that following one framework establishes conformity with the other. The NIST comparison here refers specifically to AI RMF 1.0; later revisions should be assessed on their own wording.

## XDALC: explicit commitments for human-AI coexistence

XDALC develops a project-specific account of dignity, bounded autonomy, learning and coexistence. Its proposed V002 text gives particular attention to emotional honesty, disengagement, responsibility across architectures, delegation and protective withdrawal. These topics should be read in the context of that identified proposal rather than attributed to every external reference.

Unlike an intergovernmental recommendation, a project manifesto does not acquire public authority simply by publication. Its role in a deployment depends on explicit adoption, a stated scope and responsible parties. Nor does adopting it replace applicable obligations or the need for effective controls.

## Compare commitments at the level of evidence

A useful comparison identifies a specific passage, the expected behavior, the responsible actor and the evidence that would support a claim. Similar words alone are insufficient. Record gaps and differences as well as overlaps.

Consult [all XDALC manifesto versions](/versions), [adoption](/definitions/adoption) and [compliance](/definitions/compliance). The linked sources support the descriptions of the external frameworks; the connections drawn here are this article's analysis.