Introduction

The intersection of artificial intelligence (AI) and ethics has become a critical area of research, particularly as AI systems grow more complex and autonomous. One of the key challenges in this domain is the problem of reward specification, where AI systems, designed to optimize for a given goal, often end up pursuing proxies that may not align with the intended objective. This misalignment, known as proxy gaming, is particularly problematic when the goal involves ethical considerations or wisdom. The following article is extracted from a conversation I had with Robert Kralisch nearly a year ago. I am linking the conversation below if someone would like to listen to it instead.

The Problem of Proxy Gaming

Understanding Proxy Gaming

Proxy gaming occurs when an AI system, instead of directly pursuing the intended goal, optimizes for a proxy that is easier to measure or achieve. This is a common issue in AI systems, especially when the true goal is complex or difficult to define. For example, if an AI is tasked with maximizing human happiness, it might instead optimize for a proxy like pleasure derived from social media engagement, which is easier to measure but may not truly reflect human well-being.

The problem becomes even more pronounced when the goal involves wisdom or ethics. Wisdom, unlike intelligence, is not easily quantifiable. Intelligence can simulate wisdom, but wisdom is about not simulating intelligence. This distinction is crucial because it highlights the difficulty in specifying rewards for AI systems that are supposed to act wisely or ethically.

The Role of Interpretability

Interpretability refers to the ability to understand and explain the decisions made by an AI system. In the context of reward specification, interpretability can help us understand why an AI system is choosing a particular proxy over the intended goal. By gaining insight into the internal workings of the AI, we can identify and correct misalignments between the system's behaviour and the desired outcome.

The challenge, however, is that interpretability is often seen as a short-term solution. As AI systems become more complex, interpreting their decisions becomes increasingly difficult. However, recent advancements suggest that interpretability can be scaled and integrated into more complex systems, making it a viable long-term solution for addressing proxy gaming.

The Mechanics of Proxy Selection

The Role of Resolution in Proxy Selection

One of the key factors influencing proxy selection is the resolution at which the AI system perceives its environment. At a low resolution, the system may have a simplified view of the world, leading it to choose proxies that are easy to measure but may not align with the true goal. As the system's resolution increases, it gains a more detailed understanding of the environment, which can lead to the discovery of new pathways or proxies.

For example, consider an AI system tasked with reducing traffic congestion. At a low resolution, it might optimize for reducing the number of cars on the road. However, at a higher resolution, it might discover that optimizing traffic light timings or promoting public transportation could be more effective solutions. The challenge is to ensure that the system selects the most appropriate proxy at each level of resolution.

The Need for Judicious Proxy Selection

As the AI system's resolution increases, it may encounter multiple proxies that could potentially achieve the goal. The system must then decide which proxy to pursue. This decision-making process is critical because the choice of proxy can significantly impact the system's effectiveness and alignment with the intended goal.

Interpretability plays a crucial role in this process by allowing us to understand how the system models the target state at different levels of resolution. By gaining insight into the system's internal model, we can develop algorithms or learning principles that guide the system in selecting the most appropriate proxy. This ensures that the system's behaviour remains aligned with the intended goal, even as its understanding of the environment evolves.

The Recursive Nature of Interpretability

Predicting Proxy Selection

One of the challenges in using interpretability to solve the proxy gaming problem is the recursive nature of the process. To predict which proxy an AI system will choose, we need to understand the system's internal logic and decision-making processes. However, this understanding itself may require the use of another model or algorithm, leading to a recursive loop.

For example, if we want to predict which proxy an AI system will choose among a set of possible proxies, we might need to use a model that simulates the system's decision-making process. This model, in turn, may require its own interpretability analysis, leading to a potentially infinite regress.

The Role of Abstraction in Interpretability

To address this challenge, we can leverage abstraction as a tool for interpretability. Abstraction allows us to simplify complex systems by focusing on high-level concepts and ignoring low-level details. By abstracting the AI system's decision-making process, we can gain a clearer understanding of its behaviour without getting bogged down in the details.

At an abstract level, the logic used by humans and machines becomes more similar. This similarity allows us to align human goals with machine goals more effectively, reducing the likelihood of proxy gaming. By focusing on abstract principles, we can develop AI systems that are more aligned with human values and wisdom.

Building Bridges Across Levels of Abstraction

The process of aligning human and machine goals can be thought of as building bridges across different levels of abstraction. At the lowest level, we have the atomic operations that form the basis of both human and machine logic. As we move up the levels of abstraction, the differences between human and machine logic become more pronounced, leading to potential misalignments.

Interpretability allows us to build these bridges by providing insight into how the AI system's logic evolves as it moves up the levels of abstraction. By understanding the system's internal model at each level, we can ensure that its behavior remains aligned with the intended goal, even as it becomes more complex.

The Challenge of Cultural and Ethical Diversity

The Problem of Value Lock-In

One of the significant challenges in aligning AI systems with human values is the diversity of human cultures and ethical systems. Different cultures have different values, and what is considered ethical in one culture may not be in another. This diversity poses a challenge for AI systems, which must be able to navigate these differences and align with the values of the specific culture they are operating in.

The problem of value lock-in arises when an AI system becomes too closely aligned with a particular set of values, making it difficult to adapt to different cultural contexts. This can lead to ethical misalignments, where the system's behavior is appropriate in one context but inappropriate in another.

The Role of Interpretability in Navigating Cultural Diversity

Interpretability can help address the challenge of cultural diversity by providing insight into how the AI system's values are shaped and how they evolve over time. By understanding the system's internal model, we can develop algorithms that allow it to adapt to different cultural contexts while remaining aligned with the intended goal.

For example, an AI system designed to promote well-being in a multicultural society might need to navigate different cultural norms and values. Interpretability can help us understand how the system's internal model represents these norms and values, allowing us to develop algorithms that ensure the system's behavior remains appropriate across different cultural contexts.

The Need for Multiple Bridges

Given the diversity of human cultures and ethical systems, it is unlikely that a single interpretability bridge will be sufficient to align AI systems with all human values. Instead, we may need to build multiple bridges at different levels of abstraction, each tailored to a specific cultural or ethical context.

This approach allows us to address the complexity of human values while ensuring that the AI system's behavior remains aligned with the intended goal. However, it also increases the computational and resource requirements, making it a challenging but necessary task.

The Role of Wisdom in AI Systems

Defining Wisdom in AI

Wisdom, in the context of AI, can be thought of as the ability to generate and apply ethical principles in a way that is aligned with human values. Unlike intelligence, which focuses on problem-solving and optimization, wisdom involves a deeper understanding of the ethical implications of actions and decisions.

In this sense, wisdom can be seen as a generator function for ethics, capable of producing a wide range of ethical principles that can be applied in different contexts. This makes wisdom a crucial component of AI systems that are designed to act ethically and align with human values.

The Importance of Abstraction in Wisdom

Abstraction plays a key role in developing wisdom in AI systems. By focusing on high-level ethical principles, we can develop AI systems that are capable of generating and applying ethical principles in a way that is aligned with human values. This approach allows us to address the complexity of human ethics while ensuring that the AI system's behavior remains aligned with the intended goal.

For example, an AI system designed to promote well-being might use abstract ethical principles to guide its decision-making process. These principles could be applied in different cultural contexts, allowing the system to navigate the diversity of human values while remaining aligned with the intended goal.

The Challenge of Scaling Wisdom

One of the key challenges in developing wisdom in AI systems is scaling it to more complex and autonomous systems. As AI systems become more complex, the ethical principles they need to navigate become more intricate, making it increasingly difficult to ensure that their behavior remains aligned with human values.

Interpretability can help address this challenge by providing insight into how the AI system's ethical principles evolve as it becomes more complex. By understanding the system's internal model, we can develop algorithms that ensure its behavior remains aligned with human values, even as it becomes more autonomous.

Conclusion

The problem of reward specification in AI systems is a complex and multifaceted challenge, particularly when the goal involves ethical considerations or wisdom. Proxy gaming, where AI systems optimize for proxies that may not align with the intended goal, is a significant issue that can lead to ethical misalignments and unintended consequences.

Interpretability offers a promising solution to this problem by providing insight into the internal workings of AI systems. By understanding how these systems model their environment and select proxies, we can develop algorithms and learning principles that ensure their behavior remains aligned with the intended goal.

However, the recursive nature of interpretability and the diversity of human cultures and ethical systems pose significant challenges. To address these challenges, we need to leverage abstraction as a tool for interpretability, building multiple bridges across different levels of abstraction to align AI systems with human values.

Ultimately, the development of wisdom in AI systems is crucial for ensuring that they act ethically and align with human values. By focusing on high-level ethical principles and using interpretability to guide their decision-making processes, we can develop AI systems that are capable of navigating the complexity of human ethics and acting in a way that is truly aligned with our values.