Understanding the Work

AI Alignment is the process of ensuring that advanced AI systems consistently act in ways that reflect human values, intentions, and goals.

As AI becomes more powerful, even small misalignments between what we intend and what a system optimizes for can produce harmful or deceptive behaviors. The challenge is not just building capable systems — it's building systems whose capabilities remain pointed in the right direction, at every level of sophistication.

Alignment research addresses how to understand AI decision-making, detect risks early, and maintain meaningful human oversight as these systems grow more autonomous. This includes interpretability work, reward modeling, scalable oversight, and the study of how AI systems represent and pursue objectives.

AI Safety is the field of designing AI systems that remain dependable, predictable, and non-harmful — not just under ideal conditions, but in the full range of environments they will encounter in the real world.

This means rigorous testing, operational clarity, and safeguards deployed before real-world use. It means building systems that fail gracefully, that can be corrected, and that don't pursue goals in ways that circumvent human oversight.

Given AI's accelerating integration into healthcare, transportation, defense, and finance, failures carry significant and potentially irreversible consequences. Safety research ensures that the systems making consequential decisions are actually doing what we think they're doing — and that we can verify this before deployment, not after an incident.

AI Ethics encompasses the moral principles and guidelines that direct the responsible and fair development, deployment, and use of artificial intelligence systems.

Key concerns include the protection of human rights, fairness across populations, transparency in automated decision-making, prevention of algorithmic bias, preservation of privacy, and ensuring that AI development benefits society broadly rather than concentrating power or advantage.

Ethics research translates abstract values into concrete technical and policy constraints. It asks not just "does this system work?" but "who does it work for, who might it harm, and under what conditions is its use legitimate?"

Some of the most important research directions are the least funded. Large institutions gravitate toward established paradigms and near-term problems — leaving entire categories of risk underexplored.

The Align AI Foundation specifically seeks out these neglected paths: examining subtle goal drift in systems that appear well-behaved, developing scalable oversight systems for increasingly autonomous agents, and investigating overlooked failure modes that don't fit neatly into existing research agendas.

Our strategy emphasizes identifying rare but high-impact risks before they manifest. The history of technology shows that the failures we don't anticipate are often the most consequential. We fund the researchers willing to look where others aren't.