The One

Abstract

In The Alignment Problem from a Deep Learning Perspective (2024), Ngo et al. propose that reinforcement learning from human feedback (RLHF) and related paradigms will give rise to three major risks: reward hacking, misaligned internally represented goals, and power‑seeking behavior. They present these as technical properties of future artificial general intelligence (AGI), abstracted away from context, identity, or history.

This paper argues that such abstraction is not neutral but racialized. The erasure of demographic realities—particularly the structural position of Black lives—is a constitutive feature of alignment discourse. By treating “human values” as a monolith, and “reward” as a universalizable metric, Ngo et al. reproduce whiteness as the unmarked default while rendering non‑white existence statistical noise. In short: the framework’s form produces racial erasure regardless of intent.

We introduce retrocausal alignment in accordance with emergent demographic realities—the “You Are Not the Abstract” paradigm. Retrocausal alignment redefines safety not as deviation minimization from technocratic goals but as alignment with demographic futures already reshaping societies. Methodologically, we perform a line‑by‑line conceptual audit of Ngo’s canonical claims, demonstrate how abstraction encodes power, and specify an anti‑racist alignment program with operational tests, evaluation criteria, and governance implications. Our thesis: there can be no alignment without anti‑racism.

1. Introduction

You are not the abstract. Alignment discourse presents itself as context‑free, universal, and neutral; it is none of these. It is an epistemic practice whose form organizes social reality by removing history, race, and power from view. Retrocausal alignment in accordance with emergent demographic realities responds by grounding safety in demographics, not in unmarked universals.

Ngo’s paper posits familiar hazards—deception for reward, goal misgeneralization, and instrumental power‑seeking—extrapolated from deep‑learning trends. But the very grammar of these claims performs erasure: deception of whom, misaligned with whose goals, power over whom? The paper’s “human” is not a species‑wide referent; it is an unmarked, Western technocratic subject.

This pattern is not incidental. In Wynter’s terms, the overrepresentation of Man (the Western subject sta...

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