In Stephen Ross's seminal 1973 paper, "The Economic Theory of Agency: The Principal's Problem," he provided a fresh mathematical lens to view the longstanding adversarial relationship between agents and principals. Ross's groundbreaking approach outlined key variables in agency theory, which can be applied to the rapidly growing field of artificial intelligence (AI) to address the challenges associated with aligning AI models with human objectives and interests.

In Stephen Ross's seminal 1973 paper, "The Economic Theory of Agency: The Principal's Problem," he provided a fresh mathematical lens to view the longstanding adversarial relationship between agents and principals. Ross's groundbreaking approach outlined key variables in agency theory, which can be applied to the rapidly growing field of artificial intelligence (AI) to address the challenges associated with aligning AI models with human objectives and interests.

Ross's key variables in agency theory include:

  • Recognizing the misalignment of interests between principals and agents

  • Implementing incentive contracts to align their interests

  • Addressing information asymmetry hindering optimal contract design

  • Balancing risk-sharing to match the agent's risk preferences with the principal's

Ross's key variables in agency theory include:

  • Recognizing the misalignment of interests between principals and agents

  • Implementing incentive contracts to align their interests

  • Addressing information asymmetry hindering optimal contract design

  • Balancing risk-sharing to match the agent's risk preferences with the principal's

Applying these concepts to AI, the principal-agent problem is reframed as an optimization challenge, where human principals (users, organizations) strive to maximize their expected utility within the constraints set by the AI agent's private information, risk preferences, and inherent limitations.

Applying these concepts to AI, the principal-agent problem is reframed as an optimization challenge, where human principals (users, organizations) strive to maximize their expected utility within the constraints set by the AI agent's private information, risk preferences, and inherent limitations.

As AI agent capabilities improve, their actions, and human interface will increasingly trend towards authorization and direction of the human principal. In other words, the dominant paradigm will be one of orchestration and human sign-off with agents performing tasks at scale. This shift emphasizes the need for a protocol that allows human principals to effectively communicate their objectives and preferences to AI agents, while taking into consideration the agents' constraints.

As AI agent capabilities improve, their actions, and human interface will increasingly trend towards authorization and direction of the human principal. In other words, the dominant paradigm will be one of orchestration and human sign-off with agents performing tasks at scale. This shift emphasizes the need for a protocol that allows human principals to effectively communicate their objectives and preferences to AI agents, while taking into consideration the agents' constraints.

We argue that the current AI landscape lacks a crucial layer that helps human principals confidently engage in contracts with AI agents, maximizing expected utility while addressing agents' constraints regarding asymmetric information, designed alignment, and architected risk.

We argue that the current AI landscape lacks a crucial layer that helps human principals confidently engage in contracts with AI agents, maximizing expected utility while addressing agents' constraints regarding asymmetric information, designed alignment, and architected risk.

To address this, we propose the creation of an interoperable authorization protocol that securely manages the transmission, inference, and reception of model output between agents and principal(s).

To address this, we propose the creation of an interoperable authorization protocol that securely manages the transmission, inference, and reception of model output between agents and principal(s).

This solution will build upon the principal-agent framework by providing a structured method for managing the interaction between AI agents and human principals. The interoperable authorization protocol would enable a more seamless and effective exchange of information, leading to better alignment of AI models with human objectives and interests, ultimately mitigating the challenges associated with the principal-agent problem in the context of AI. It is important to note that this is somewhat orthogonal to foundational helpfulness vs harmlessness alignment. Regardless of the base model’s ability, this protocol should serve to improve agent efficacy in real world tasks via communication and authorization.

This solution will build upon the principal-agent framework by providing a structured method for managing the interaction between AI agents and human principals. The interoperable authorization protocol would enable a more seamless and effective exchange of information, leading to better alignment of AI models with human objectives and interests, ultimately mitigating the challenges associated with the principal-agent problem in the context of AI. It is important to note that this is somewhat orthogonal to foundational helpfulness vs harmlessness alignment. Regardless of the base model’s ability, this protocol should serve to improve agent efficacy in real world tasks via communication and authorization.

The primary interface with AI will soon be iterative feedback and authorization, with the principal setting objectives and authorizing agent actions. We believe it’s crucial to create a solution that is model agnostic to standardize the practices of how human principals will authorize AI agents. This protocol will also be designed to facilitate information exchange, consent management, and inter-model communication.

The primary interface with AI will soon be iterative feedback and authorization, with the principal setting objectives and authorizing agent actions. We believe it’s crucial to create a solution that is model agnostic to standardize the practices of how human principals will authorize AI agents. This protocol will also be designed to facilitate information exchange, consent management, and inter-model communication.

Standardized communication protocols and data formats will help different AI models work together more effectively, while consent management systems can ensure that human preferences are consistently respected across different AI systems. Additionally, by incentivising principal agent cooperation, we hope to push towards an open and vibrant ecosystem where everyone has access to useful and aligned agents.

Standardized communication protocols and data formats will help different AI models work together more effectively, while consent management systems can ensure that human preferences are consistently respected across different AI systems. Additionally, by incentivising principal agent cooperation, we hope to push towards an open and vibrant ecosystem where everyone has access to useful and aligned agents.

To address the principal-agent problem in AI, we propose a single, holistic protocol called the Interoperable Authorization Protocol (IAP), which includes the following components:

To address the principal-agent problem in AI, we propose a single, holistic protocol called the Interoperable Authorization Protocol (IAP), which includes the following components:

  1. Advanced Secure Communication: Using cutting-edge, standardized, and widely adopted methods, the protocol will enable secure communication and data transfer between AI agents and principals, ensuring smooth compatibility between various AI systems.

  1. Advanced Secure Communication: Using cutting-edge, standardized, and widely adopted methods, the protocol will enable secure communication and data transfer between AI agents and principals, ensuring smooth compatibility between various AI systems.

  1. Consent Control System: The protocol will incorporate a consent management system, allowing principals to set preferences and manage AI agent permissions, ensuring agents adhere to human values and objectives while minimizing misaligned results and unauthorized activities.

  1. Consent Control System: The protocol will incorporate a consent management system, allowing principals to set preferences and manage AI agent permissions, ensuring agents adhere to human values and objectives while minimizing misaligned results and unauthorized activities.

  1. Incentive Synchronization Mechanisms: The protocol will integrate mechanisms that align agent incentives with principal objectives, including performance-based rewards, penalties for misaligned actions, and dynamic risk-sharing adjustments based on agent performance and principal risk preferences.

  1. Incentive Synchronization Mechanisms: The protocol will integrate mechanisms that align agent incentives with principal objectives, including performance-based rewards, penalties for misaligned actions, and dynamic risk-sharing adjustments based on agent performance and principal risk preferences.

  1. Transparent Audit Trails: The protocol will establish clear reporting and auditing processes, allowing principals to assess AI agent performance, monitor guideline compliance, and make informed decisions about permissions, fostering trust and collaboration between principal and agent.

  1. Transparent Audit Trails: The protocol will establish clear reporting and auditing processes, allowing principals to assess AI agent performance, monitor guideline compliance, and make informed decisions about permissions, fostering trust and collaboration between principal and agent.

  1. Adaptability Policies: The protocol will encourage AI agents to learn from actions, outcomes, and principal feedback, continuously adapting to changes and improving alignment with human values and objectives, addressing the principal-agent dilemma.

  1. Adaptability Policies: The protocol will encourage AI agents to learn from actions, outcomes, and principal feedback, continuously adapting to changes and improving alignment with human values and objectives, addressing the principal-agent dilemma.

For the IAP to effectively address the principal-agent problem in the constantly changing AI landscape, it is imperative to open-source the protocol, making it accessible to the global development community. Open-sourcing is the most viable approach to guarantee widespread adoption, stimulate innovation, and allow developers to expand, tailor, and enhance the protocol for rapid deployment and integration in diverse AI applications.

For the IAP to effectively address the principal-agent problem in the constantly changing AI landscape, it is imperative to open-source the protocol, making it accessible to the global development community. Open-sourcing is the most viable approach to guarantee widespread adoption, stimulate innovation, and allow developers to expand, tailor, and enhance the protocol for rapid deployment and integration in diverse AI applications.

We encourage fellow AI researchers, developers, and stakeholders to join us in exploring and refining the Interoperable Authorization Protocol concept. We can work towards creating a robust, adaptable, and secure solution that effectively addresses the principal-agent problem in AI, paving the way for a future where AI models and systems are truly aligned with human values and objectives. Let's collaborate and be a measurable force in directing AI's tremendous potential for the betterment of humanity.

We encourage fellow AI researchers, developers, and stakeholders to join us in exploring and refining the Interoperable Authorization Protocol concept. We can work towards creating a robust, adaptable, and secure solution that effectively addresses the principal-agent problem in AI, paving the way for a future where AI models and systems are truly aligned with human values and objectives. Let's collaborate and be a measurable force in directing AI's tremendous potential for the betterment of humanity.

Andrew Carr

Nate Sanders

Caleb bartholomew

akarsh gupta

BERIC BEARNSON

SKYLER LEWIS

Andrew Carr

Nate Sanders

Caleb bartholomew

akarsh gupta

BERIC BEARNSON

SKYLER LEWIS

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Are you interested in being a contributor?