Written in collaboration with Claude-3.5.-Sonnet. All information checked by human author.
Background
Because of the utility and value of large language models (LLMs) in tasks supporting persona creation and users’ interaction with personas, chat-based AI personas are becoming increasingly prevalent. However, these personas present unique challenges that can be understood through the lens of agency theory (also known as the principal-agent theory). This post explores how the principal-agent framework from economics can be applied to AI-generated personas, offering insights into their behavior and limitations.
Key vocabulary:
- Users = this means people using the personas (e.g., designers, marketers, and other decision makers)
- End-users = these are the people personas represent (i.e., personas are created to represent the views of specific end-user groups)
The Double Agency Problem
When considering AI-generated personas, we encounter a fascinating double layer of agency:
- Chat (LLM) as the agent of the persona: The chat interface acts as an agent for the underlying persona.
- Persona as the agent for end-users: The persona itself acts as an agent for the end-users it represents.
In economics, the principal-agent problem often arises due to information asymmetry. The agent (chat interface) may have more information about its actions than the principal (underlying persona data). This can lead to suboptimal outcomes, similar to how a CEO (agent) might act in ways that don’t fully align with shareholders’ (principals) interests. In particular, two issues emerge:
- Moral Hazard: The AI persona may “slack off” or not put in full effort to represent users accurately, especially if it’s not being monitored or evaluated effectively. Unlike with human agents, this “slacking off” is not intentional, but it is nonetheless harmful.
- Adverse Selection: Because users (principals) may not have full information about the AI persona’s capabilities, leading to suboptimal “hiring” of AI assistants for tasks. In other words, we do personas with LLMs, the results *look* good, and so we continue on basis of trust, oblivious of the underlying issues.
Possible Solutions
In economics, contract theory suggests that carefully designed incentives can align the interests of principals and agents. For AI personas, this might translate to:
- Reward functions that encourage accurate representation of the underlying data
- Feedback mechanisms that allow users to “train” the AI to better represent their interests
In addition, agency theory emphasizes the importance of monitoring agent performance. In the context of AI personas, this raises questions about:
- How to measure the fidelity of the chat interface to the underlying persona
- Metrics for evaluating how well the AI persona serves user needs
Implications and Future Research
By framing AI-generated personas within the context of agency theory, we open up new avenues for research and development:
- How can we design “contracts” or reward structures for AI that minimize agency problems in both layers?
- What role can transparency play in reducing information asymmetry between users and AI personas?
- Can we develop AI governance structures that mirror corporate governance solutions to agency problems?
The rich body of economic theory on principal-agent problems offers a useful framework for addressing these challenges.