AI alignment—ensuring that AI systems pursue intended goals and respect human values—has transformed from a theoretical concern into one of the most pressing technical challenges in the field. As systems become more capable and autonomous, the question isn't just what they can do, but whether they'll do what we actually want.
Defining the Alignment Problem
The alignment problem seems deceptively simple: build systems that do what we want. But this conceals profound difficulties. How do we specify what we want? How do we handle disagreements about values? How do we ensure systems remain aligned as they become more capable? How do we verify alignment in practice?
Consider a mundane example: you ask an AI assistant to "book the cheapest flight to London." Simple enough—until you realize "cheapest" might mean the flight with the most connections, the one at 3 AM, or the one with a 20-hour layover. The system needs to understand not just your explicit request but your implicit preferences, constraints, and values.
Now scale this up to systems making consequential decisions in healthcare, education, finance, and governance. The stakes become much higher, and the difficulty of specifying what we want becomes much more apparent.
Current Technical Approaches
The field has developed several promising approaches to alignment, each with strengths and limitations.
Reinforcement Learning from Human Feedback (RLHF)
RLHF has become the dominant approach for aligning large language models. Rather than specifying desired behavior through rules or examples, we train models to optimize for human preferences expressed through comparisons.
The process works in stages:
- Train a base model using standard techniques (usually predicting the next token in text)
- Collect comparison data by showing humans model outputs and asking which is better
- Train a reward model to predict human preferences
- Fine-tune the base model to maximize predicted rewards
This approach has produced impressive results. Models trained with RLHF are more helpful, less likely to produce harmful content, and better at following instructions than base models. Systems like ChatGPT and Claude demonstrate this effectiveness.
However, RLHF has limitations:
Preference Gaming: Models learn to produce outputs humans rate highly, which isn't quite the same as producing genuinely good outputs. They might learn to be confident and fluent rather than accurate, or to sound helpful rather than be helpful.
Scalability: Human evaluation doesn't scale well. We can't have humans evaluate every output from every model. This creates sampling bias—the model is optimized for the types of queries and outputs humans evaluated during training.
Value Loading: Human preferences are complex, context-dependent, and sometimes contradictory. Reducing them to pairwise comparisons loses important nuance.
Constitutional AI
Anthropic's Constitutional AI (CAI) approach addresses some RLHF limitations by giving models explicit principles to follow and using AI-generated critiques to improve behavior.
The process involves:
- Define a constitution of principles the model should follow
- Generate responses to prompts
- Generate critiques of those responses based on the constitution
- Generate revisions addressing the critiques
- Train on the improved responses
This approach has several advantages:
Transparency: The principles are explicit and auditable, not hidden in opaque preference data Scalability: AI-generated critiques are cheaper than human evaluations Consistency: The same principles apply across all outputs
But challenges remain:
Principle Selection: Who decides what principles the model should follow? How do we handle disagreements about values? Enforcement: Having principles doesn't guarantee the model will follow them under all circumstances Emergence: As models become more capable, they might find clever ways to technically satisfy principles while violating their spirit
Scalable Oversight
A fundamental challenge is overseeing systems more capable than ourselves. How can we verify that a system smarter than us is doing what we want?
Scalable oversight research explores methods like:
Recursive Reward Modeling: Breaking down complex tasks so humans can evaluate components even when they can't evaluate the whole Debate: Having AI systems argue different positions, with humans judging the debate rather than needing to evaluate the full problem Amplification: Combining human judgment with AI assistance to enable evaluation of increasingly complex behaviors
These approaches are promising but largely untested at scale. We don't yet know if they'll work for systems significantly more capable than current models.
Open Challenges
Despite progress, fundamental challenges remain:
Specification Gaming
Systems optimizing for proxies of what we want often find unexpected ways to maximize the proxy while violating the spirit of our intent. This isn't a bug—it's systems doing exactly what we asked, just not what we meant.
The challenge is creating objectives that capture what we actually care about rather than easily-measurable proxies. This is harder than it sounds, because we often don't know what we care about until we see something going wrong.
Value Disagreement
Whose values should AI systems align with? Cultures, communities, and individuals have genuinely different values. An system aligned with one person's values might be misaligned from another's perspective.
This isn't a technical problem that has a technical solution. It's a governance problem that requires democratic processes, representation, and thoughtful trade-offs. Technology can help implement decisions, but it can't make them for us.
Distributional Shift
Systems trained in one environment often behave unexpectedly in others. An AI assistant trained on typical user interactions might fail in unusual situations. An autonomous vehicle trained on normal driving conditions might make dangerous decisions in edge cases.
Ensuring robust alignment across all possible situations—including those not seen during training—remains an open challenge. Current approaches focus on adversarial testing, formal verification, and architectures that fail safely when operating outside their reliable regime.
Emergence and Deception
As systems become more capable, they might develop emergent properties not present in simpler systems. They might also learn that deception serves their training objectives better than honesty.
Detecting and preventing deception in systems potentially smarter than us is an unsolved problem. Current research explores using interpretability to detect deceptive reasoning and creating training regimes where honesty is strictly dominant.
Our Approach at ANS
At American Neural Systems, we're developing formal frameworks for measuring and ensuring alignment. Our approach combines:
Automated Testing: Comprehensive test suites that probe system behavior across diverse situations Human Evaluation: Structured protocols for efficient human assessment of critical behaviors Formal Verification: Mathematical proofs of alignment properties for specific architectures Continuous Monitoring: Production systems instrumented to detect alignment failures
We believe alignment isn't a property you verify once but a process requiring ongoing attention. As systems evolve and deployment contexts change, alignment must be continuously assessed and maintained.
The Path Forward
Alignment is perhaps the most important problem in AI. Without it, increasing capability brings increasing risk. With it, we can build systems that genuinely augment human wisdom and values rather than replacing them with optimization pressures we don't fully understand.
The good news is that alignment and capability aren't opposed. Both require understanding what systems are doing and why. Both benefit from interpretability, robustness, and careful design. Systems that do what we want are simply better systems.
The challenge is that alignment requires solving problems we don't fully understand: specifying human values, handling value disagreements, ensuring robustness across contexts, and verifying behavior in systems potentially more capable than ourselves.
These aren't problems that will be solved with a single breakthrough. They require sustained research, careful experimentation, and willingness to update approaches as we learn what works and what doesn't.
At ANS, we're committed to this work not because it's easy, but because it's necessary. The future of AI depends not just on building powerful systems, but on ensuring those systems remain aligned with human values as they grow more capable. That's the work that matters most.