November 03, 2025
As AI systems become increasingly capable and integrated into critical infrastructure—from healthcare to finance, the question of safety has moved from theoretical concern to urgent priority. But what exactly do we mean by “AI safety,” and how should we think about solving it?
The core function of AI safety is to ensure that artificial intelligence systems operate as intended and do not cause significant, unintended, or irreversible harm to individuals, groups, or humanity as a whole.
This primary function can be broken into two main branches:
In essence, AI safety is the field of engineering, computer science, and policy dedicated to building robustly beneficial systems and managing the risks of powerful technology.
AI safety is not a single property. It is a multidimensional problem space. The key sub-problems that must be managed are:
Alignment: How do we ensure that an AI system can capture our values and reliably execute actions matching our preferences. As systems become more capable and more optimizing for the wrong objective—even one that seemed reasonable initially—could be catastrophic. Brian Christian’s popular book titled The Alignment Problem does a terrific job of describing this problem.
Example: An AI told to “end cancer” might see killing all humans as a 100% effective solution. The objective was followed, but the intent was violated.
Robustness: This is the dimension of a system’s ability to maintain safe behavior when it encounters novel situations, unexpected inputs, or adversarial attacks.
Example: A self-driving car’s safety model must be robust to new road-sign patterns, “hacked” sensor data, or weather conditions it was not trained on.
Interpretability (or Transparency): How do we understand what an AI system has learned and why it makes particular decisions? Without this understanding, we can’t identify potential failures before they occur. If a powerful AI is a “black box,” we cannot debug it, trust it, or predict its failures.
Example: An AI that denies loan applications must be able to provide a human-understandable reason for its decision.
Controllability: How do we maintain meaningful human oversight and guarantee iur ability to correct or shut down AI systems, even as they become more capable than us?. A key safety challenge is ensuring a highly intelligent system does not learn to resist being corrected or shut down. I particularly like Stuart Russell’s exposition of this problem in his book Human Compatible. Russell argues that the traditional approach to AI – optimizing a fixed, known objective function is fundamentally flawed. Instead, he proposes that AI systems should be uncertain about human preferences and learn them through observation and interaction.
These sub-problems of AI safety aren’t independent, progress in one often supports progress in others, and failures can cascade across multiple dimensions simultaneously.
It’s worth clarifying how AI safety relates to other important concerns like AI fairness, which often comes up in safety discussions.
AI fairness addresses the problem of discriminatory outcomes—ensuring that AI systems don’t systematically disadvantage particular groups based on protected characteristics like race, gender, or age. When a hiring algorithm screens out qualified candidates from underrepresented groups, or when a loan approval system denies credit disproportionately to certain demographics, we have a fairness problem.
AI safety, while related, has a broader scope. Fairness issues represent one type of harm (discriminatory harm), but safety encompasses many other failure modes. A language model that generates equally terrible medical advice for everyone is fair but deeply unsafe.
The relationship between fairness and safety is hierarchical. Fairness is a critical component of safety. You can’t have a truly safe AI system that systematically harms particular groups. But safety encompasses additional concerns beyond fairness: robustness to adversarial attacks, alignment with human values, containment of capable systems, and avoiding catastrophic failures. Think of fairness as a necessary but not sufficient condition for safety.
This distinction matters because it clarifies what we’re working on. Fairness research primarily operates in the space of the Accident Problem (ensuring systems don’t unintentionally harm specific groups), while connecting to multiple dimensions—especially alignment (are we optimizing for the right values?) and interpretability (can we detect biased reasoning?).
To address each of the above mentioned problems in AI safety, we find the various strategies, frameworks, and theoretical approaches researchers have developed to solve them.
Value Alignment Approaches
These mechanisms aim to ensure AI systems learn and optimize for the right things:
Robustness and Reliability Mechanisms
These approaches ensure systems behave safely even in unexpected situations:
Oversight and Control Mechanisms
These maintain human agency over AI systems:
Transparency Mechanisms
These help us understand what AI systems are doing:
Each algorithmic approach makes different tradeoffs between safety guarantees, capability, and computational cost. Some work well for current systems but may not scale to more capable AI. Others provide stronger guarantees but are computationally intractable for complex real-world systems. The algorithmic level is where we balance these tradeoffs and develop practical frameworks for implementation.
The implementation level is where theory meets practice—the actual tools, techniques, and code that researchers and practitioners use to build safer AI systems. If you’re wondering where hot topics like mechanistic interpretability and AI red teaming fit into the grand scheme, this is their home.
Mechanistic Interpretability
This emerging field sits at the implementation level, providing concrete techniques for understanding neural networks. Rather than treating networks as black boxes, researchers reverse-engineer them to identify specific circuits and mechanisms:
Mechanistic interpretability implements the algorithmic-level goal of transparency. When we discover that a language model has developed “induction heads” that implement in-context learning, or identify the specific attention patterns that enable factual recall, we’re building the foundation for safer systems. We can’t fix what we can’t see.
Tools like TransformerLens and the techniques developed by Anthropic’s interpretability team exemplify implementation-level work. They take abstract interpretability goals and turn them into concrete Python libraries and experimental protocols.
AI Red Teaming
Red teaming—systematically probing AI systems for failures and vulnerabilities—is another crucial implementation-level technique. It operationalizes the algorithmic goal of robustness testing:
Organizations like Anthropic, OpenAI, and Google DeepMind maintain dedicated red teams. These teams implement concrete testing protocols: they write code to generate adversarial inputs, develop playbooks for manual testing, and create benchmarks to track progress over time.
Reinforcement Learning from Human Feedback (RLHF)
RLHF has become the dominant approach for aligning large language models with human preferences. At the implementation level, it involves:
Each component requires careful engineering. How do you prevent reward models from being exploited? How do you make training stable when your reward signal comes from another neural network? These aren’t algorithmic questions—they’re implementation challenges that practitioners solve through trial, error, and careful system design.
Constitutional AI and Related Techniques
Anthropic’s Constitutional AI (CAI) exemplifies how algorithmic principles translate into implementation practice. The high-level idea—have AI systems critique and revise their own outputs according to principles—gets realized through:
The code that samples critiques, the format of constitutional principles, the hyperparameters for training these implementation details matter enormously for whether the approach works in practice.
Model Evaluation and Benchmarking
You can’t improve what you don’t measure. Implementation-level work includes building the infrastructure to evaluate safety:
Each benchmark requires careful construction: selecting diverse examples, avoiding data leakage, ensuring the metric actually captures what matters. This is painstaking implementation work that enables all higher-level safety efforts.
Sandboxing and Containment Tools
As AI systems become more capable, we need concrete mechanisms to limit their potential for harm:
Building these tools requires thinking through threat models, designing robust access control systems, and testing that containment measures actually work under adversarial pressure.
Open Source Safety Tooling
The implementation level increasingly involves building reusable tools that make safety work more accessible:
These tools lower the barrier to safety research and make it easier for practitioners to build safer systems.
Understanding AI safety through the perspective of the core problems, mechanisms and tools helps newcomers to the field better understand the relationships between issues:
Different levels require different expertise: Problem identification and specification involves philosophy, ethics, and decision theory. Algorithmic-level work requires deep machine learning and theoretical CS expertise. Implementation-level work needs software engineering skills and practical ML experience. We need all three.
Progress in one aspect enables progress at others: When Russell clarified the computational problem (build systems uncertain about human preferences), it opened space for algorithmic work (CIRL, assistance games). When researchers developed algorithms for learning from preferences, it enabled implementation work (RLHF pipelines, preference interfaces).
Confusion often arises from mixing terms: When someone asks “does mechanistic interpretability solve alignment?” they’re mixing levels. Interpretability is an implementation-level technique that helps us achieve algorithmic-level transparency goals, which in turn address computational-level challenges around understanding and controlling AI systems. It’s essential but not sufficient.
A model can reveal gaps: Strong problem statement and understanding without algorithmic-level solutions leaves us with well-articulated problems we can’t solve. Sophisticated algorithms without implementation-level tools remain theoretical. Implementation-level techniques without clear algorithmic and computational grounding risk solving the wrong problems efficiently.
If you’re just getting started in AI safety, this three-level framework provides a roadmap:
Start by understanding the problem. Read Russell’s “Human Compatible,” Paul Christiano’s work on alignment, and Nick Bostrom’s “Superintelligence.” Grapple with the deep questions: what are we trying to achieve, and why is it hard?
Study algorithmic-level approaches. Dive into specific research agendas: CIRL, iterated amplification, debate, factored cognition. Understand the tradeoffs different approaches make and why researchers believe they might work.
Get hands-on with implementation. If you want to contribute, this is where to start. Pick up mechanistic interpretability, try red teaming models, build safety evaluation benchmarks. The field desperately needs people who can turn ideas into working code.
The best safety work keeps all three aspects in mind. Implementation efforts should connect to clear algorithmic strategies that address genuine computational-level problems. Theoretical breakthroughs should suggest concrete implementation paths.
The alignment problem won’t be solved by any single technique or insight. It will be solved by sustained effort across all three levels: philosophical clarity about what we’re trying to achieve, rigorous algorithmic frameworks for how to achieve it, and robust implementation tools that turn theory into practice.
The stakes are high, the problems are hard, and we need all everyoone involved. Understanding where your work fits in the bigger picture and how the pieces connect is the first step toward meaningful contribution.