Corpus Governance

This research highlights a recursive trap: the very discourse surrounding AI alignment acts as a form of "alignment pretraining," embedding narrow socio-technical biases into models before a single line of RLHF code is even run.▶ Discourse as Training Data: AI alignment is not merely an algorithmic fix; it is a performative act where the language used to describe "safety" dictates the model's latent worldviews during pretraining.▶ The Technocratic Echo Chamber: By over-indexing on technical existential risks while sidelining socio-political nuances, current alignment efforts risk creating models that are "aligned" only to a narrow, Western-centric technocracy, creating a self-fulfilling prophecy of what AI should be.Bagua InsightAt 「Bagua Intelligence」, we view this as a massive, unintended feedback loop. The Silicon Valley "safety" narrative is being ingested by the very models it seeks to control. This creates a "hallucination of consensus" where models mirror the biases of the researchers who built them, not because of explicit tuning, but because those researchers' papers and debates dominate the pretraining corpus. We aren't just building AI; we are building a mirror of our own industry's limited perspective. The risk is that we are hardcoding a specific ideological framework into the "base intelligence" of future systems, making genuine value pluralism nearly impossible to achieve post-hoc.Actionable AdviceOrganizations must diversify their pretraining data sources beyond mainstream tech discourse to include marginalized perspectives and non-technical humanities. Developers should treat "alignment" as a socio-technical challenge rather than a purely optimization-based one. It is critical to conduct "discursive audits" on base models to identify where pretraining data has already locked in specific ideological biases before proceeding to fine-tuning stages.