AI Alignment, Explained: The Technical and Normative Problems—and How OpenAI and Anthropic Are Tackling Them

AI alignment is the challenge of ensuring advanced AI systems reliably pursue goals beneficial to humans, a problem relevant today for systems like chatbots. The field faces both technical (how to inscribe values) and normative (which values to inscribe) challenges, with leading labs like OpenAI and Anthropic developing distinct strategies.

Rui Almeida

October 20, 2026 · 7 min read

The Dual Challenge: Technical vs. Normative Problems

A scholarly chapter on human value alignment by Ilias O. Pappas and Polyxeni Vassilakopoulou, published by Springer Nature Singapore, frames the field's central difficulty precisely: technical challenges concern how to inscribe values in AI, while normative challenges concern what values to inscribe. The two layers compound each other. Values are inherently diverse across cultures and stakeholders, abstract values are difficult to operationalize into concrete AI behaviors, and existing alignment techniques carry real limitations that researchers must continually refine. The chapter's authors group implementation approaches into three overarching categories—design-based, technical, and regulatory—and conclude that progress requires interdisciplinary collaboration among researchers, industry practitioners, and policymakers.

The technical problem is fundamentally one of translation. Human intentions are often implicit, contextual, and contested; a training pipeline needs them expressed as objectives a model can actually optimize. An instruction like "be helpful" must become a measurable training signal, and the gap between the abstract value and the concrete behavior is where misalignment creeps in. The normative problem is one of legitimacy and selection: even a flawless technical method must still be pointed at some particular set of values, and no consensus mechanism exists for choosing among the diverse values held by different cultures and stakeholders.

Notably, even the labs themselves acknowledge that the normative question sits outside their technical programs. OpenAI states that aligning AI systems with human values poses a range of significant sociotechnical challenges—such as deciding to whom these systems should be aligned—and explicitly sets those problems aside from its technical research discussion. The Springer chapter, for its part, stresses that alignment methodologies need continuous refinement rather than one-time solutions, a point both labs' public statements echo in their own vocabularies.

OpenAI's Approach to Alignment Research

OpenAI's published approach to alignment research rests on three main pillars: training AI systems using human feedback, training AI systems to assist human evaluation, and training AI systems to do alignment research. The first pillar—reinforcement learning from human feedback, or RLHF—is its primary technique for aligning deployed language models, training them to follow human intent by adhering to both explicit instructions and implicit human preferences. In other words, the method tries to capture not only what users say they want but what they actually prefer, and to bake both into the model's behavior.

OpenAI is candid about the limits of that foundation. It does not expect RLHF to be sufficient for aligning AGI, but treats it as a core building block for the scalable alignment proposals it finds most promising—which is why it considers perfecting the methodology valuable. The reason for the limitation is structural: RLHF assumes that humans can accurately evaluate the tasks their AI systems are doing. Today humans are pretty good at this, OpenAI says, but as models become more capable, they will perform tasks that are much harder for humans to judge. OpenAI's own examples include finding all the flaws in a large codebase or in a scientific paper—work where a human reviewer cannot simply inspect the output and confirm it is correct.

That gap motivates the second pillar: training models to assist human evaluation. OpenAI's stated interest is in training models that help humans distinguish correct solutions from misleading or deceptive ones, because alignment techniques need to keep working even when AI systems propose very creative solutions—the company invokes AlphaGo's famous move 37 as the kind of unexpected, human-inscrutable output its methods must survive. OpenAI adds that it believes the best way to learn how to make AI-assisted evaluation work in practice is to build AI assistants, making this an empirical, build-and-iterate program rather than a purely theoretical one.

The third pillar is the most ambitious. OpenAI states plainly that there is currently no known indefinitely scalable solution to the alignment problem, and that as AI progress continues, it expects to encounter new alignment problems not yet observed in current systems. Its response is to train AI systems to do alignment research themselves—leveraging AI capabilities to accelerate the search for solutions to the very problem they pose. OpenAI also frames transparency as part of the program, saying it wants to be transparent about how well its alignment techniques actually work in practice and wants every AGI developer to use the world's best alignment techniques.

Anthropic's Alignment Science Directions

Anthropic's framing starts from a premise about the future rather than the present: future AI systems will be even more powerful than today's, likely in ways that break key assumptions behind current safety techniques. That premise is why the company considers it important to develop sophisticated safeguards ensuring models remain helpful, honest, and harmless. Its Alignment team works to understand the challenges ahead and to create protocols to train, evaluate, and monitor highly capable models safely, operating alongside research teams in areas including interpretability, economics, societal impacts, and frontier red teaming.

Within that structure, a core activity is evaluation and oversight: alignment researchers validate that models are harmless and honest even under circumstances very different from those in which they were trained. This addresses a specific failure mode—models that behave well in training conditions but not in the shifted conditions of real deployment—and treats robustness of good behavior, not just average performance, as the object of study.

Anthropic's Alignment Science team also publishes recommendations for the broader research community, aimed at mitigating the risk of catastrophes caused by future advanced AI systems—risks it describes in stark terms, including mass loss of life or permanent loss of human control. The team's central challenge, as it states, is identifying concrete technical work that can be done today to prevent these risks, in future worlds that will have been radically transformed by AI development. The team notes that it often encounters AI researchers interested in catastrophic risk reduction who struggle with the same question: what technical research conducted today will actually be useful to developers ensuring the safety of their future systems?

The resulting document is explicitly bounded. To produce it, the team asked its members to write descriptions of open problems or research directions they considered important, then organized the results into broad categories. The team calls the result not a comprehensive list but a "tasting menu" of interesting open problems—and, importantly, not a list of directions it is actively working on or supporting. While the team does conduct research in some of these areas, many are directions it would like to see progress in but lacks the capacity to invest in itself. The stated hope is to stimulate more work from the broader AI research community.

Among the concrete technical directions the team highlights are improving the faithfulness of chain-of-thought reasoning and developing better control mechanisms for AI systems—specific problems aimed at preventing undesirable emergent behaviors in more capable future systems.

Comparing Leading AI Alignment Research Strategies

The table below categorizes and compares the publicly stated research pillars of OpenAI and Anthropic by their primary goals and methods, so the reader can see at a glance that alignment is not one approach but several distinct fronts of work.

Publicly stated alignment research pillars and directions at OpenAI and Anthropic
Research pillar / approach	Description and goal	Primary organization
Training AI with human feedback (RLHF)	Primary technique for aligning deployed language models, training them to follow human intent—both explicit instructions and implicit human preferences. OpenAI does not consider it sufficient for AGI but treats it as a core building block for scalable proposals.	OpenAI
Training AI to assist human evaluation	Using AI systems to help humans evaluate model behavior as tasks exceed direct human judgment—for example, distinguishing correct solutions from misleading or deceptive ones—making oversight more efficient and effective in complex systems.	OpenAI
Training AI to do alignment research	Leveraging AI capabilities themselves to contribute to and accelerate alignment research, motivated by the absence of any known indefinitely scalable solution and the expectation of new, unobserved alignment problems.	OpenAI
Developing sophisticated safeguards for future models	Creating protocols to train, evaluate, and monitor highly capable models so they remain helpful, honest, and harmless—critical because future systems may invalidate the assumptions behind current safety techniques.	Anthropic
Validating harmlessness and honesty under shifted circumstances	Evaluation and oversight research validating that models remain harmless and honest even in conditions very different from those in which they were trained.	Anthropic
Identifying open problems for the research community	Publishing a "tasting menu" of recommended open problems—such as improving chain-of-thought faithfulness and developing better control mechanisms—aimed at mitigating catastrophic risks from advanced AI and stimulating outside work.	Anthropic

Read side by side, the strategies reveal a shared diagnosis with a difference in architecture. Both organizations assume current techniques will not scale indefinitely: OpenAI says there is no known indefinitely scalable solution, and Anthropic says future systems will likely break key assumptions behind current safety techniques. But OpenAI's pillars form an explicitly escalation-driven ladder—human feedback today, AI-assisted evaluation as tasks outgrow human judgment, and finally AI doing the alignment research itself. Each rung exists because the previous one has a known failure mode: RLHF fails when humans cannot evaluate outputs, and human-plus-assistant evaluation must still withstand creative, potentially deceptive solutions.

Anthropic's stated work, by contrast, is organized around robustness and community problem-setting rather than a single escalation logic. Its safeguards pillar targets the gap between training conditions and deployment conditions; its open-problem recommendations explicitly outsource work the team cannot do itself, aiming to grow the field's capacity rather than only its own. The two programs are complementary in an important sense: OpenAI's third pillar—AI doing alignment research—presupposes that someone has defined which research problems matter, which is precisely the function Anthropic's tasting menu performs for the broader community.

Navigating the Future of Aligned AI Development

Readers should recognize that AI alignment is a complex, evolving field with multiple active research fronts, requiring ongoing engagement with the specific strategies of leading organizations. No source here claims a solved problem. OpenAI frames its pillars as building blocks toward scalable proposals it does not yet have; Anthropic frames its recommendations as a deliberately non-comprehensive menu meant to catalyze work it cannot itself perform. Both framings are, in themselves, evidence of the field's defining condition: the technical challenge of operationalizing values and the normative challenge of selecting among diverse stakeholder values remain open, and the labs' own statements treat them as such.

Two boundaries on this picture deserve emphasis. The comparison above rests on publicly available research statements, not on internal or undisclosed projects, so it describes each lab's stated strategy rather than its complete activity. And it deliberately leaves aside the philosophical question of which specific human values should be instilled—a question the Springer chapter shows remains contested across cultures and stakeholders, and which OpenAI itself flags as a sociotechnical challenge it has not addressed in its technical program.

The measurable indicator to watch is the release of new research papers or public statements from OpenAI or Anthropic detailing advancements or shifts in their stated alignment research pillars. When OpenAI reports on whether AI-assisted evaluation or AI-driven alignment research is working in practice, or when Anthropic's Alignment Science team revises its recommended directions—adding, retiring, or taking up items from its tasting menu—those publications will mark where the field's assumptions are changing. Until then, the pillars described here are the concrete, checkable record of how two leading labs say the alignment problem is being tackled.