13 · Open Data Layer, Sources, and Acknowledgments

13.1 · Architecture: method layer decoupled from data layer

EINS is an assessment-method layer. It does not produce, own, or sell the underlying data. Its inputs are, as a backbone, third-party open data, and, as a supplement, self-collected public data (e.g., its own observed series for some metrics, marked as such). The correct positioning sentence is: EINS is an assessment method rather than a data product, using third-party open data as its backbone and self-collected public data as a supplement. Because the method layer does not compete with its sources, the system's sources are not adversaries; they are the foundation of its fairness. The decoupling has an institutional mechanism, not just a stated intention. The circuit breakers and redundancy of Section 6.2 make any single source replaceable, and the source-swap migration procedure (Appendix D) is a drilled capability. “We do not compete with our sources” is therefore backed by “we cannot be held hostage by any one of them,” which is the actual guarantee of source neutrality.

13.2 · FA source acknowledgments

The FA fundamental field is built on the following institutions and source families, and the system gratefully acknowledges them: Hugging Face (open model hub and the Open LLM Leaderboard), GitHub (open-source repository metrics), SWE-bench, Aider, and LiveCodeBench (coding-capability evaluations), LMArena (crowd pairwise comparisons, in the Elo-rating tradition (Elo, 1978; Zheng and others, 2026)), and OpenAlex (citation data for the research ripple). The academic literature underpinning the measurement design (Sections 1–2) and the methodological precedents of influence indexing — notably the Stanford AI Index and Epoch AI's analyses — are cited in the references.

13.3 · RI sources: deliberately unnamed

The RI signal consumes “independently verified authoritative media and public platform information,” and the individual sources are not named. The reasons are stated plainly: naming media and social-platform sources would (a) create an avoidable legal exposure surface around the collection of public signals and (b) hand an attacker a short-horizon arbitrage map against a component that is already deliberately opaque (Section 7.5). The methodology is described in full at the structural level (Section 6.6); the calibration is not disclosed. Because the paper discloses neither the sources nor the numerical parameters, the RI description is a description of a mechanism, not a specification that could be gamed; that is the intended epistemic posture of asymmetric disclosure (Section 7.2).

13.4 · Publisher attribution

The system is published under a role designation rather than an individual name, for a single reason: fairness of assessment. An evaluation of EINS and the Eins Index should turn on the methodology, the formal structure, the data, and the evidence — not on who published them. Identity-blind assessment is the established device for keeping scrutiny on content rather than provenance; the equity rationale of blind peer review is exactly this (Pleskac et al., 2026), and a measurement system has the same need. The choice withholds only the publisher's identity: the FA methodology, the inclusion rules, and the reference parameter tables are given in full (Appendices B–C), while the RI layer remains deliberately undisclosed. Accountability lives in the published method, not in a byline. There is also a symmetry to it: a system that measures influence should not mint a personal-influence asset out of its own publisher. The request is therefore simple: judge EINS and the Eins Index on their own merits, and build on the method layer if it earns your confidence.

13.5 · Openness commitments

Openness is asymmetric and deliberately modest. The FA methodology and reference parameter tables (Appendices B–C) are documented in this white paper, and FA-related data is intended to be made available to the extent feasible. RI implementation details and the live-signal parameters are reserved. The stated posture is that the system's architecture is open to scrutiny (its invariants are stated publicly and are intended to be checkable), while specific open-scope and open-timing decisions remain under review. Third parties are invited to build on the method layer and to contribute data-layer collaboration. These statements are made without promises of specific scope or timing, because a commitment that outruns its institutional support is itself a failure mode; the architecture is the commitment, and the specific releases follow from it as they become sustainable.

13.6 · Disclaimer

EINS is a research social-influence measurement system, built for the benefit of public understanding of AI; it is not a commercial project and is not designed for financial use. The Eins Index and its family are research artifacts. They do not constitute financial information, investment advice, a credit assessment, a reputation judgment, or a basis for individual decisions; no action should be taken on the basis of the index without independent verification. The observation window is short and the method is under active iteration. Event attribution and grading include human adjudication, which is subject to revision. Source availability affects coverage. The index reflects what has already happened; it is not a prediction. The live-signal component is deliberately non-reproducible by external parties. The system is an independent third-party evaluation, not an endorsement of any measured Originator, and it does not use the logos of the measured organizations. Legal compliance is addressed in Appendix H. Nothing in this white paper is a promise of any specific index behavior, openness scope, or release timing.

Acknowledgments

The system is grateful to the institutions listed in Section 13.2 for maintaining the open data on which the FA field depends, and to the research community whose evaluation methodology and influence-measurement literature (Sections 1–2) frame this work.

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