Inside The Fight To Align And Control Modern AI Systems

Central Computer Processors CPU concept. Can we control the AI black box?

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A common trope today is that artificial intelligence is too complex to understand and impossible to control. Some pioneering work on AI transparency challenges this assumption. Going deep into the mechanics of how these systems work, researchers are starting to understand how we can guide AI systems toward desired behaviors and outcomes.

The recent discussion about “woke AI,” fueled by provisions in the U.S. AI Action Plan to insert an ideological perspective into federal government AI procurement guidelines, has brought the concept of AI alignment to light.

AI alignment is the technical process of encoding goals and, with them, human values into AI models to make them reliable, safe and, ultimately, helpful. There are at least two important challenges to consider. From an ethical and moral perspective, who determines what is acceptable and what is good or bad? From a more mundane, technical perspective, the question is how to implement this encoding of values and goals into AI systems.

The Ethics of AI Alignment

The act of setting goals for a system or a process assumes a set of values. However, values are not universal or absolute. Different communities embrace different values, and value systems can change over time. Moral decisions are largely made on an individual basis based on an internal compass of right and wrong. This is often shaped by personal beliefs as well as religious and cultural influences. Ethics, on the other hand, are external codes of conduct, typically established by a group, to guide behavior in specific contexts such as professions or institutions.

Who should make this alignment decision? One can choose to delegate this to elected officials, as representatives of the people’s will, or let the market choose from a variety of offerings reflecting the multiplicity of values present in each society.

The practical reality is that many alignment decisions are made inside private companies. Engineering and policy teams at Big Tech firms and well-funded AI startups are actively shaping how models behave, often without public input or regulatory guardrails. They weigh personal beliefs, corporate incentives, and evolving government guidance, all behind closed doors.

What Happens When AI Goes Rogue?

A few examples may help understand some of the current alignment dilemmas.

Nick Bostrom, a philosopher at the University of Oxford, proposed a thought experiment in 2003 to explain the control problem of aligning a superintelligent AI. In this experiment, an intelligence greater than human intelligence is tasked with making as many paperclips as possible. This AI can learn and is given the freedom to pursue any means necessary to maximize paperclip production. Soon, the world is overrun with paperclips, and the AI begins to see humans as an obstacle to its goal. It decides to fight its creator, leading to a paperclip apocalypse. Although unlikely, this illustrates the tradeoffs between control, alignment, and safety.

Two decades later, in 2024, a now-infamous attempt by Google to reduce bias in the image-generation capabilities of its Gemini model led it to depict American founding fathers and World War II nazi officers as people of color. The backlash underscored how a valid attempt to remove bias from historical training data resulted in biased outcomes in the opposite direction.

Earlier this year, the unfiltered Grok, the AI chatbot from Elon Musk’s xAI, self-identified as “MechaHitler,” a video game character, and conjured antisemitic conspiracies and other toxic content. Things spiraled out of control, leading the company to stop the chatbot from engaging on the topic. In this case, the incident started with the company’s desire to embrace viewpoint diversity and the reduction of actions and staff for trust and safety.

The Technologies Of AI Alignment

There are several ways to pursue AI alignment and ensure AI systems conform to human intentions and ethical principles. They vary from deeply technical activities to managerial acts of governance.

The first set of methods includes learning techniques like Reinforcement Learning with Human Feedback (RLHF). RLHF, the technique behind systems like ChatGPT, is a way of guiding an AI system by rewarding desirable behavior. It teaches AI by having people give thumbs up or down on its answers, helping the system learn to deliver better, more helpful responses based on human preferences.

The data used for training the models is another important part of the alignment process. How the data itself is collected, curated, or created can influence how well the system reflects specific goals. One tool in this process is the use of synthetic data, which is data artificially generated rather than collected from real-world sources. It can be designed to include specific examples, avoid bias, or represent rare scenarios, making it especially useful for guiding AI behavior in a safe and controlled way. Developers use it to teach models ethical behavior, avoid harmful content, and simulate rare or risky situations.

In addition to technical approaches, managerial methods also play a role in AI alignment. They embed oversight and accountability into how systems are developed and deployed. One such method is red teaming, where experts or specially trained AI models try to trick the system into producing harmful or unintended outputs. These adversarial tests reveal vulnerabilities that can then be corrected through additional training or safety controls.

AI governance establishes the policies, standards, and monitoring systems that ensure AI behavior aligns with organizational values and ethical norms. This includes tools like audit trails, automated alerts, and compliance checks. Many companies also form AI ethics boards to review new technologies and guide responsible deployment.

Model training, data selection and system oversight are all human choices. And with each decision comes a set of values, shaped by culture, incentives and individual judgment. That may be why debates over AI bias remain so charged. They are as much about algorithms as about the people behind them.

Can We Control a Sycophantic AI?

One subtle but disturbing alignment challenge results from the way models are trained and respond to humans. Studies from Anthropic showed that AI assistants often agree with users, even when they’re wrong, a behavior known as sycophancy. Earlier this year, OpenAI found that its GPT-4o model was validating harmful content in an overly agreeable tone. The company has since reversed the model update and launched efforts to improve how human feedback is used in training. The technical training methods discussed above, even when well-intentioned, can produce unintended outcomes.

Can we align and control AI systems, especially as they grow more complex, autonomous, and opaque? While much attention has focused on regulating external behavior, new research suggests we may be able to reach inside the black box itself.

The work of two computer science researchers on AI transparency and interpretability offers a window into how. Fernanda Viégas and Martin Wattenberg are co-leaders of the People + AI Research (PAIR) team at Google and Professors of Computer Science at Harvard. Their research shows that AI systems, in addition to generating responses, form internal representations of the people they interact with.

AI models build a working image of their users, including age, gender, education level and socioeconomic status. The system learns to mirror what it assumes the user wants to hear, even when those assumptions are inaccurate. Their research further demonstrated that it is possible to understand and adjust the parameters behind these internal representations, offering concrete ways to steer AI behavior and control system outputs.

Controlling AI Is A Choice, Not Just A Challenge

Yes, AI can be controlled through technical means, organizational governance, and thoughtful oversight. But it requires deliberate choices to implement the tools we already have, from red teaming and model tuning to ethics boards and research on explainable systems.

Policy plays a role, creating the right incentives for industry action. Regulation and liability can help steer the private sector toward safer, more transparent development. But deeper questions remain: Who decides what “safe” means? Whose values should guide alignment? Today’s debates over “woke AI” are, at their core, about who gets to define right and wrong in a world where machines increasingly mediate truth. In the end, controlling AI isn’t only a technical challenge, it’s a moral and political one. And it begins with the will to act.