Ethical and Legal Limits of Aligning Large Language Models: The Illusion of Control

Le résumé fourni par la source

Alignment has become the dominant paradigm for governing large language models (LLMs), implemented through post-training optimization, safety fine-tuning, and inference-time steering. These approaches are commonly assumed to translate normative intent into reliable system behavior. As LLMs are increasingly deployed as large-scale decision-support and information systems, however, the limits of this assumption have become evident. This chapter analyzes alignment from a systems and governance perspective, examining how alignment and steering mechanisms interact with scale, infrastructure, and deployment dynamics. We argue that contemporary approaches primarily regulate observable output behavior through probabilistic narrative control rather than enforcing durable epistemic constraints. While effective at shaping refusals, explanations, and interaction norms, they do not guarantee robustness, truthfulness, or stability under distribution shift or strategic pressure. As a result, aligned systems may appear compliant while remaining epistemically fragile. Current legal and ethical governance frameworks, which emphasize documentation, transparency, and design-time intent, struggle to address this gap because they operate upstream of the deployment-time mechanisms that shape behavior in practice. Situating alignment within the global political economy of AI, the chapter shows how capital concentration, infrastructural lock-in, and competitive deployment amplify these limitations by redistributing effective control toward actors with access to compute, data, and platforms. These pressures feed back into model behavior, producing internal failure modes such as sycophancy, over-alignment, and calibration collapse. We conclude by reframing alignment as a bounded interface-level control mechanism, highlighting the need for system-level oversight beyond training-time alignment alone.

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