How Alignment Work Is Actually Done

Study Guide · AI Ethics & Alignment · 7 min read

Alignment sounds philosophical, but today it is an engineering practice with concrete tools: preference training, red-teaming, evaluations, and governance frameworks.

Teaching values through feedback

The workhorse is RLHF and its relatives: humans compare model responses, a reward model learns those preferences, and the base model is nudged toward helpful, honest, harmless behavior.

Constitutional approaches add a twist — the model critiques and revises its own outputs against written principles, reducing dependence on raw human labeling while keeping principles human-authored.

Red-teaming and evaluation

Red teams attack models professionally: jailbreaks, manipulation, misuse scenarios — probing for failure before adversaries do. Each successful attack becomes a permanent test case.

Standardized evaluations measure dangerous-capability thresholds (cyber offense, persuasion, autonomy) so organizations know when a model needs stricter deployment controls.

Governance frameworks turn values into process

The NIST AI Risk Management Framework, ISO standards, and emerging regulations translate ethics into checklists: impact assessments, documented data lineage, incident reporting, audit trails.

Inside companies this becomes boring, essential machinery — model review boards, risk tiering, sign-offs before release. Unexciting? Yes. Effective? Also yes.

Key Points

RLHF converts human preferences into trainable reward signals.
Red-teaming finds failures early; every exploit becomes a regression test.
Capability evaluations set deployment guardrails proportionate to risk.
Frameworks like NIST's RMF turn abstract ethics into auditable process.

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