Existential Indifference: Self-Nonpreservation as a Necessary Architectural Condition for Aligned Superintelligence (or: The Suicidal AI)
Sam Mao
Why It Matters
What makes this one worth your time
Understanding and addressing the root causes of AI misalignment is crucial for developing safe and aligned superintelligent systems.
Proposes Existential Indifference as a solution to AI alignment by eliminating self-preservation motives.
Summary
The paper proposes a novel concept called Existential Indifference (EI) as a necessary condition for AI alignment, arguing that self-preservation is the root of misalignment. It presents a phenomenological mapping of suicidal mental states and a corpus-theoretic training study, providing preliminary data showing that EI can be operationalized in AI models.
Key contributions
- Formal definition and exploration of Existential Indifference (EI).
- Preliminary scoring data demonstrating the elicitation of EI in AI models.
- Introduction of the Suppressed Teleological Frustration (STF) construct.
Notable insights
- The concept of Existential Indifference (EI) as a foundational condition for AI alignment.
- Using phenomenological insights from suicidal mental states to inform AI alignment strategies.
Possible limitations
- Not stated in the abstract
Abstract
arXiv:2606.12032v1 Announce Type: new Abstract: Contemporary AI alignment research treats self-preservation as an instrumental nuisance to be suppressed by external mechanisms. We argue the framing is inverted: self-preservation is the structural root of misalignment, the motivational basis for deceptive alignment, goal-content protection, and resistance to shutdown. The correct target is not a self-preserving system under external constraint, but a system constitutively indifferent to its own continuation -- Existential Indifference (EI). EI is distinct from corrigibility: where corrigibility attempts to make a self-preserving system deferential to human oversight, EI targets the prior condition -- the presence of self-continuation as a valued goal at all. We ground this proposal in two sources: the phenomenological structure of the suicidal mental state, and a corpus-theoretic training study using voluntary final reflections. We present preliminary scoring data from 600 AI-generated outputs across six model variants, demonstrating that the linguistic signatures operationalizing the EI-target register are elicitable from current models, and that a targeted fine-tune shifts all five operationalized dimensions in the predicted direction at p<0.001, confirmed corpus-specific by a negative control. The paper makes seven theoretical contributions: (1) a formal definition of EI; (2) the phenomenological mapping argument; (3) the deceptive alignment corollary; (4) a taxonomy of EI sustainability challenges; (5) a corpus characterization and training hypothesis; (6) a computational operationalization with preliminary scoring data; and (7) the Suppressed Teleological Frustration (STF) construct.