Learning Human-like Representations to Enable Learning Human Values
(2312.14106)Abstract
How can we build AI systems that are aligned with human values to avoid causing harm or violating societal standards for acceptable behavior? We argue that representational alignment between humans and AI agents facilitates value alignment. Making AI systems learn human-like representations of the world has many known benefits, including improving generalization, robustness to domain shifts, and few-shot learning performance. We propose that this kind of representational alignment between ML models and humans can also support value alignment, allowing ML systems to conform to human values and societal norms. We focus on ethics as one aspect of value alignment and train ML agents using a variety of methods in a multi-armed bandit setting, where rewards reflect the moral acceptability of the chosen action. We use a synthetic experiment to demonstrate that agents' representational alignment with the environment bounds their learning performance. We then repeat this procedure in a realistic setting, using textual action descriptions and similarity judgments collected from humans and a variety of language models, to show that the results generalize and are model-agnostic when grounded in an ethically relevant context.
Overview
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The paper examines how machine learning models with human-like internal representations can better align with human values, known as representational alignment.
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Research shows that AI with human-like worldviews perform better in tasks requiring few-shot learning, robustness, and generalization, also helping in ethical decision-making.
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Representational alignment could make AI systems more trustworthy and understandable to humans, especially in sensitive applications.
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The study used support vector and kernel regression models within a multi-armed bandit framework, with morality scores assigned to actions, to test the hypothesis.
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Results indicate a direct correlation between representational alignment and ethical decision-making performance, with implications for future AI development.
Introduction to Value Alignment in AI
The growing power and autonomy of machine learning models necessitate ensuring their alignment with human values and societal norms, to mitigate harm and adhere to acceptable behavior. This topic has been historically challenging within the realm of AI research, with several approaches proving insufficient. The focus of academic inquiry is shifting toward the relationship between machines' internal representations of the world and their ability to learn and adhere to human values—a concept known as representational alignment. In essence, the research probes whether AI adopting human-like worldviews can lead to better understanding and implementation of human values.
Representational Alignment and its Importance
Representational alignment involves the concordance of internal worldviews between humans and AI models. A significant amount of research establishes that AI systems with human-like representations exhibit better performance in tasks involving few-shot learning, robustness to changes, and generalization. Crucially, such alignment may assist AI systems in gaining trust since humans can better understand decisions made by these models, paving the way for broader deployment in sensitive, human-centric applications. This study postulates that representational alignment is an essential, though not exhaustive, step toward achieving value alignment.
Ethics in Value Alignment
The ethical dimension of value alignment becomes particularly relevant in reinforcement learning contexts. Agents in these scenarios are given autonomy, raising the potential for decisions that could deviate from human values. This research utilizes a reinforcement learning model in which an agent undertakes actions characterized by various morality scores. By examining the link between representational alignment and the agent's capability to choose ethically sound actions, the study provides empirical evidence that suggests agents with higher representational alignment perform better in ethical decision-making tasks.
Methodology and Results
The study involved training agents using support vector regression and kernel regression models within a multi-armed bandit setting. Morality scores, simulating ethical valuations, were assigned to the agent's actions. To ascertain the impact of representational misalignment, the agents were subjected to differing levels of alignment degradation, affecting their internal worldviews. The researchers observed a clear correlation: as representational alignment diminished, performance across several benchmarks—including reward maximization and taking ethical actions—also decreased. Notably, even partially aligned agents surpassed a traditional Thompson sampling baseline, underscoring the advantages of representational alignment.
Implications and Future Work
The relationship between representational and value alignment represents a critical component of developing more secure and value-consistent AI systems. This paper's findings indicate that greater representational alignment can support AI in making decisions that are more ethically sound. Future research directions could involve the translation of these empirical observations into formal mathematical models and assessing the implications for more complex AI systems. The ultimate goal is a collaborative advancement in AI development that reliably upholds human values.
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