AI Alignment

Summary

The technical problem of designing AI systems whose objectives, behaviour, and emergent goals reliably track the values or instructions of their principals across deployment contexts.

At a glance

Used by

7 instrument(s)

Related concepts

deceptive-alignment, mesa-optimization, scalable-oversight, capability-elicitation, red-team-evaluation

Primary source

Yudkowsky, E. (2008), 'Artificial Intelligence as a Positive and Negative Factor in Global Risk' — the field-foundational articulation of the alignment problem.

Source URL

https://intelligence.org/files/AIPosNegFactor.pdf

Details

Alignment, in the technical sense, is distinct from regulatory 'compliance' or 'safety.' It asks: even if a model is capable and even if it is supervised, does it pursue what its principal actually wants — or does it pursue a proxy objective that diverges in edge cases? The problem decomposes into outer alignment (specifying what we want the model to do — see Krakovna et al.'s 'specification gaming' literature) and inner alignment (whether the model trained on that specification actually internalised it — see Hubinger et al. 2019 on mesa-optimisation). Governance instruments rarely use the word 'alignment' directly. EU AIA Art. 51-55 obligations approximate alignment concerns by mandating systemic-risk assessment + adversarial testing + cybersecurity protection, but do not require demonstrated alignment of model objectives. US EO 14110 §4.2(a) mandated reporting on alignment-relevant capabilities (red-team results) without defining 'alignment.' Anthropic, OpenAI, and DeepMind publish their own alignment research agendas; these are de facto cited in policy debates but absent from binding text. The field treats alignment as a research problem first and a governance object only secondarily.