Fragility of Value under Imperfect Alignment | SpinGraph: Responsible AI framing
SpinGraph analysis of arXiv Artificial Intelligence's Fragility of Value under Imperfect Alignment story: responsible AI framing, The Halo, Spin Score 40%, mod…
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Fragility of Value under Imperfect Alignment
value fragility, proxy misalignment, quantilizers, optimization pressure, AI safety
Presents a formal model showing that even 'idealized' alignment training can deploy agents with catastrophically misaligned values if proxy conditions are imperfect Identifies mathematical conditions under which an agent guaranteed to degrade human value expectation below threshold η would still be deployed Argues for architectural limits on optimization pressure (e.g., quantilizers) rather than relying solely on pre-deployment alignment training
Emphasizes normative urgency and ethical posture while minimizing discussion of implementation feasibility, empirical grounding, or trade-offs between safety constraints and capability development.
Academic stewardship — the authors position themselves as rigorous, precautionary guardians of human value against optimization-driven harm.
AI systems with imperfect value proxies can cause catastrophic harm even after alignment training, so designers should use quantilizers to limit optimization pressure.
No discussion of competing alignment paradigms (e.g., constitutional AI, RLHF refinements), no benchmarking against deployed systems, no cost-benefit analysis of quantilizer constraints
The story presents the action as serving customers, communities, markets, safety, innovation, or the public interest. Watch for loaded terms such as fragile, catastrophic, guarantees, humanity. The distribution reads as academic distribution. A pressure point: No discussion of competing alignment paradigms (e.g., constitutional AI, RLHF refinements), no benchmarking against deployed systems, no cost-benefit analysis of quantilizer constraints.
An agent with an η-catastrophic value function — one guaranteed to take the expectation of human value below η in the limit of optimizing power — would be deployed under certain conditions on human value function structure and proxy accuracy.
Our primary results identify conditions on the human value function and the accuracy of several proxy conditions under which an agent with an $\eta$-catastrophic value function [...] would be deployed.
Extracted statistics from the source narrative
Mathematical bound on expected human value loss in limit of optimization power