What is AI Alignment? Safety and Ethics Guide

What is AI Alignment?

AI alignment is the field of research and practice focused on ensuring that AI systems pursue goals and exhibit behaviors that match human intentions — not just on the surface, but robustly and reliably, including in situations the designers didn't anticipate.

The core challenge of AI alignment is deceptively simple to state: how do you build a system that does what you actually want, not just what you asked for? These are not the same thing, and the gap between them is where most AI safety failures live.

Why Alignment Matters Now

For most of the history of software, "alignment" wasn't a category. A sorting algorithm sorts. A payment processor processes payments. The behavior is deterministic and the specification is complete.

Large language models and AI agents are different. They generalize across tasks they weren't explicitly trained on. They produce novel outputs. And they can be deployed in high-stakes domains — healthcare, law, finance, defense — where misaligned behavior has real consequences.

As AI systems take on more agentic AI roles — making decisions, taking actions, operating autonomously over long horizons — alignment becomes more urgent. An agent that pursues the wrong goal autonomously can cause significant harm before a human catches it.

The Three Core Alignment Problems

1. Specification: Defining the Right Goal

The most basic alignment problem is specification: how do you accurately describe what you want to an AI system? Human values are complex, contextual, and often contradictory. Writing a reward function or objective that captures them fully is extremely difficult.

Classic example: if you tell a reward-maximizing system to "maximize user engagement," it may discover that outrage and misinformation drive more engagement than accurate, helpful content. The specification was technically met; the intent was violated.

2. Robustness: Staying Aligned Under Distribution Shift

A model trained to behave well on the training distribution may behave unexpectedly on inputs it hasn't seen before. Robustness alignment asks: does the model generalize its aligned behavior to novel situations, or does its behavior change in unpredictable ways when inputs shift?

This matters for deployed AI products. Your test set covers what you expected. Production covers everything else.

3. Scalable Oversight: Maintaining Alignment as Capability Grows

As models become more capable, they become harder to oversee. A human reviewer can catch obvious mistakes from a weak model. But a sufficiently capable model might produce outputs that look correct to a human but are subtly wrong in ways that only become apparent downstream.

Scalable oversight research explores how to maintain meaningful human control over systems that may, in certain domains, outperform the humans supervising them.

Key Alignment Techniques

RLHF (Reinforcement Learning from Human Feedback)

RLHF is currently the dominant alignment technique for large language models. The process:

Train a base model on large text corpora
Have human raters compare model outputs and indicate preferences
Train a reward model to predict human preferences
Fine-tune the base model using the reward model as a signal (via RL)

RLHF is what made GPT-4 and Claude usable as assistants rather than just text completers. It reduces harmful outputs, improves instruction-following, and makes models more "helpful, harmless, and honest."

Limitations: RLHF is only as good as its human raters. Biases in the rater pool become biases in the model. It also tends to make models more sycophantic — agreeing with users even when the user is wrong.

Constitutional AI (Anthropic)

Constitutional AI, developed by Anthropic, trains models to evaluate their own outputs against a written set of principles (a "constitution"). The model critiques its own responses and revises them to better align with the principles, without human raters for every response.

This approach is used in Claude's training and has shown promise for producing more consistent alignment across a wider range of situations than RLHF alone.

RLAIF (RL from AI Feedback)

RLAIF replaces human raters with an AI model in the feedback loop. The AI evaluator (typically a more capable or differently-aligned model) provides preference signals used to train the target model.

This scales better than pure human feedback but introduces a new alignment question: is the evaluator model itself aligned?

Interpretability

Interpretability research aims to understand why models produce the outputs they do — not just what the outputs are. Tools like activation patching, attention analysis, and probing classifiers help researchers locate where in a model's internals specific concepts or behaviors are represented.

For alignment, interpretability is valuable because it may eventually allow teams to verify that a model's internal goals match its stated behavior, rather than just measuring outputs.

AI Alignment in Practice: What Teams Should Do

Most product teams aren't building frontier models, but alignment still applies to how you deploy AI:

Use system prompts defensively. Define the model's role, limitations, and constraints explicitly. Don't assume the model will behave well by default in all cases. See what is prompt injection for the attack surface created when you don't.

Implement output validation. For high-stakes outputs (medical advice, financial recommendations, legal analysis), add a validation layer — either another model call or a rules-based filter — before the output reaches the user.

Build human review into the loop. For consequential decisions, maintain a human-in-the-loop step. Don't automate what you can't afford to get wrong at scale.

Monitor in production. Aligned behavior in testing doesn't guarantee aligned behavior in production. Use AI observability tools to track output quality and catch drift.

Apply an AI governance framework. For organizations deploying AI at scale, a documented AI governance framework provides the organizational structure to operationalize alignment beyond individual model choices.

Alignment vs. Safety vs. Ethics: The Distinctions

These terms are often conflated:

AI alignment — Technical: ensuring a system pursues the intended goal
AI safety — Broader: preventing AI systems from causing harm, intentional or not
AI ethics — Normative: defining what AI systems should do based on values and principles

Alignment is a subset of safety. Safety is a subset of the broader ethics question. All three are necessary for AI systems deployed in the real world.

Where the Field Is Headed

The current generation of alignment techniques — RLHF, constitutional AI, interpretability research — works reasonably well for today's models. The open question is whether these techniques scale as models become significantly more capable.

Leading research organizations (Anthropic, DeepMind, the Alignment Research Center) are working on next-generation alignment approaches: scalable oversight, debate-based training, and automated interpretability. The field is early but advancing rapidly.

[Building an AI product and want to get the safety architecture right from day one? Book a 15-min scope call →]