Problem. On social media platforms and search engines, what becomes visible, and what goes viral, is decided by recommendation, ranking, and amplification algorithms. During elections, referendums, declared public emergencies, and other internal crises, a change in the algorithmic configuration that actually runs can alter the reach of civic information at national scale within hours. Once an item has been amplified to millions of people, the effect cannot be undone. Where present solutions fall short. Today’s accountability rests on deployment approvals, model version records, signed software, audit logs, and periodic compliance reviews. These records show what was approved or what was installed. They do not show which configuration actually produced a particular ranked feed or recommendation. They also cannot stop an unauthorized output before it reaches users. A configuration can be approved upstream while a fallback model, experiment router, cache, or edge service substitutes, reorders, or redirects the output on an unverified path. Retrospective audit can find such a change only after the amplification has already happened. Solution. This work describes an implementation-neutral architecture built on the Algorithmic Logic Fingerprint (ALF), which has three parts: ALF-I identifies an authorized algorithmic configuration. ALF-E records protected measurements of the components and configurations that actually executed. ALF-O binds that execution evidence to a specific pending output. A protected validation domain issues a cryptographically authenticated validation receipt. Authority to make the output effective is set up separately as a scoped, non-bearer finality enablement. An independent Finality Sink at the effectuation boundary (gateway, CDN or edge, network egress, or device rendering) reconstructs the actual pending operation. It then checks the receipt, the enablement, the current crisis-mode generation, revocation state, and route coverage, and consumes the authority atomically before permitting the output. When any check fails, the output is withheld rather than delivered. What is new. The architecture treats algorithmic virality as a sequence of individually verifiable amplification operations. It separates computation, validation, and authority: computation is not authority to make an output effective. It models internal crises as authenticated, generation-numbered operating modes that untrusted workloads cannot switch. Organic speech is protected structurally. A failed algorithmic amplification check does not block a user’s direct publication. Heterogeneous proprietary algorithms can be verified without requiring a common ranking model and without disclosing source code, model weights, or ranking parameters. The work sets out 22 enforcement points across model activation, distributed ranking stages, delivery routes, streaming feeds, and device displays. It also provides a structured counter-verification procedure for disputed decisions. Its technical relevance is discussed for the EU Digital Services Act (Articles 34, 35, 37, and 40), the EU Artificial Intelligence Act (Articles 9 and 12 to 15), India’s Information Technology Act, 2000, with the Intermediary Guidelines Rules, 2021, as amended, and the WTO Agreement on Technical Barriers to Trade (Articles 2.1, 2.2, and 2.4). These references describe possible technical relevance only and do not establish legal compliance. The architecture does not prescribe political viewpoints, judge the truthfulness of speech, or require algorithmic uniformity: algorithmic accountability does not require algorithmic uniformity. This work makes no allegation of electoral manipulation, unlawful interference, or regulatory violation by any platform, company, government, political organization, or individual. Companion IETF Internet-Draft: draft-das-social-media-virality-accountability-00.
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Sangam Das (2026) studied this question.
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