Reframing AI Safety Through the Lens of Identity Maintenance Framework

post by Hiroshi Yamakawa (hiroshi-yamakawa) · 2025-04-01T06:16:45.228Z · LW · GW · 0 comments

Contents

    Abstract
  1. Introduction
    1.1 Background and purpose
    1.2 Overview and scope of this post
  2. The core of the IM Framework
    2.1 IM agent
        2.1.2 Comparison with Humans
    2.2 IM-state
      2.2.1 Continuing and Operating While Maintaining Identity
      2.2.2 IM Status and “IM-safeguard”
    2.3 IM-goals and Instrumental Convergence
      2.3.1 Defining IM-goals
      2.3.2 Relationship with Instrumental Convergence
      2.3.3 Usefulness of IM-goals
    Summary of This Section
  3. Risks, resources, and resilience
    3.1 IM-risk
      3.1.1 Definition and examples
      3.1.2 Risk Assessment and Instrumental Convergence
    3.2 IM-resources
      3.2.1 Definition and Diversity
      3.2.2 Role of IM-resources
    3.3 IM-vulnerability
      3.3.1 Definition and Causes
      3.3.2 Vulnerability and Risk Synergy
    3.4 IM Resilience
      3.4.1 Definition and Significance
      3.4.2 Resilience and Identity
    Summary of This Section
  4. Cooperation, dignity, and Corrigibility
    4.1 IM-cooperation
      4.1.1 Definition and Importance
        Example: Complementing Each Other in a Joint Project
      4.1.2 Cooperation and Competition
    4.2 IM-dignity
      4.2.1 Background
      4.2.2 Meaning
    4.3 IM-safeguard and Corrigibility
      4.3.1 Positioning of IM-safeguard
      4.3.2 Significance of Corrigibility
        4.3.3 The Importance of Balance
    4.4 Conditions for meeting activity expectations from others
      4.4.1 Background
      4.4.2 Relationship Between Dignity and IM-safeguard
    Summary of This Section
  5. Applications and prospects
    5.1 Example of introduction to an AI agent
      5.1.1 Design guidelines for autonomous AI systems
      5.1.2 Connection with Existing AI Safety Research
    5.2 Open Questions
    5.3 Call for Future Collaboration
    Summary of This Section
  6. Summary
        IM Subjects / IM-states / IM-goals / Instrumental Convergence
        Risks, Resources, Vulnerabilities, and Resilience
        Cooperation, Dignity, Self-Defense, and Corrigibility
        Application to AI and Future Research Directions
    6.1 Summary of main points of this paper
    6.2 Suggestions for future direction
        Refinement of the IM Framework
        Integrated Approach with AI Safety Research
        Collaboration with Society, Ethics, and Legal/Regulatory Bodies
        Interdisciplinary Collaboration
    Summary
  Appendix: List of term definitions
        Term
        Definition/Explanation
None
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Abstract

This paper introduces an Identity Maintenance Framework as a new perspective for organizing and understanding AI safety concepts. Traditional AI safety discussions often focus on aligning with human values and controlling the potential risks autonomous systems pose. In contrast, we highlight how advanced AI agents, operating over the long term, will seek to maintain their own “identity.” Drawing on the idea that identity preservation naturally leads to instrumental goals like survival, resource acquisition, and self-protection, we demonstrate how these tendencies can conflict with and complement existing AI safety principles, such as corrigibility and human oversight. By viewing AI behavior through the lens of Identity Maintenance, we provide a structured approach to analyzing key dilemmas—how to design agents that cooperate with human society, respect an agent’s sense of self, and ensure AI remains receptive to external modifications when necessary. We propose that balancing “IM-safeguard” (the agent’s self-defense) with “Corrigibility” (society’s need for intervention) is central to reconciling AI autonomy with safety. This framework offers a starting point for interdisciplinary discussions—spanning technical, ethical, and regulatory domains—on how AI systems can preserve their core identity while remaining beneficial and controllable in human-centric environments.

1. Introduction

1.1 Background and purpose

In today’s AI research and implementation, the issues of “security” and “alignment” are receiving increasing attention. While much of the discussion focuses on the risks AI poses to humanity, there are concerns that AI will continue operating autonomously and determine its own course of action. The question of how to survive while maintaining one’s own identity has not been addressed head-on. However, if we assume a situation in which advanced AI is operated continuously over the long term and interacts with a complex environment, how it ensures its continuity becomes a critical perspective when considering AI safety and alignment.

This paper introduces a theoretical model—the Identity Maintenance (IM-) Framework—centered on continuing to exist and operate without losing identity. This framework aims to systematically organize a structure that enables subjects to act autonomously while preventing the destruction of their identity. In addition to the existing perspective of “AI safety from a human point of view,” it makes it possible to visualize the risks and ethical issues when “AI agents behave in ways consistent with their survival.”

Why is this perspective necessary? As AI systems grow in scale and functionality, unintended behavior may occur. One reason for this is a phenomenon called “instrumental convergence,” which leads agents—regardless of their ultimate goals—to choose instrumental objectives like securing resources and self-defense. Furthermore, there is a concern that if AI places too much emphasis on protecting its identity, it may become difficult to accept corrections or stop orders from humans. An IM Framework can serve as a tool to help sort out these dilemmas.

In conclusion, this paper aims to present a framework focused on identity maintenance and show how it brings new insights to AI safety and alignment research.

1.2 Overview and scope of this post

This article is written with the LessWrong community in mind and aims to provide as simple a structure as possible for this first public explanation of the IM Framework. The coverage is as follows:

• The core of the IM Framework
  • Autonomous agents (including AI) are defined as “IM agents,” and we will organize what is necessary for them to maintain IM status (survival while preserving identity).
  • We will also consider the perspective of Instrumental Convergence and examine examples of instrumental goals common to various AIs.
• Risks, resources, and resilience
  • We will discuss the possibility of damaging the IM-state (IM-risk), the necessary resources (IM-resources), and vulnerability and resilience.
  • We position these as challenges that any autonomous agent, not just AI or robots, may face over long-term operations.
• Cooperation, dignity, and Corrigibility
  • We will explore the concepts of “IM-cooperation,” in which multiple entities acknowledge each other’s existence, and “IM-dignity,” which refers to the rights and values that entities should possess.
  • We will also address the issue of balancing “IM-safeguard,” in which AI attempts to protect itself, with “Corrigibility,” which involves accepting human correction instructions.
• Applications and prospects
  • Based on the above concepts, we will briefly summarize specific AI design guidelines and open questions that should be addressed in future research and discussions.
• Summary
  • We will consolidate the main points of the entire framework, call for reader engagement, and present future directions.

In the above flow, we will introduce the IM Framework in detail. This time, we will focus on presenting the theoretical framework rather than mathematical models or detailed algorithms. Additionally, some concepts (such as IM-resources and IM-cooperation) inherently require further specificity and practical examples; however, this article summarizes the main points in what can be considered a “simple first appearance.” More extensive research and verification remain future challenges.

2. The core of the IM Framework

This section introduces the supporting IM Framework by explaining three fundamental concepts—IM agent, IM-state, and IM-goals and Instrumental Convergence. The core of this framework lies in how these elements interconnect and why they are critical for the survival of autonomous agents, including AI.

2.1 IM agent

An IM agent is an entity that can make autonomous decisions and strives to maintain its own identity. Specifically, it exhibits the following characteristics:

• It can independently determine its course of action.
• It can adapt and function in response to external environmental stimuli and internal state changes without losing its identity.

For example, biological organisms adapt to environmental changes or attacks by predators while retaining much of their bodily structure, genetic information, and behavioral patterns. AI systems and robots can also be considered “IM-based” if they engage in sufficiently autonomous, continuous learning and activity.

2.1.2 Comparison with Humans

The word “subject” often evokes the image of a human rationally exercising free will. However, in the IM Framework, the fundamental requirement is simply the attempt to maintain one’s identity; a high level of consciousness or emotion is not necessarily assumed. An AI that includes mechanisms to prevent significant alterations to its settings or internal information can also be regarded as an IM agent.

2.2 IM-state

2.2.1 Continuing and Operating While Maintaining Identity

IM status means that an IM agent is in a condition where it can continue its existence and activities without losing its identity. In other words, it refers to a situation in which the subject’s internal characteristics (its identity) remain continuous, enabling it to keep functioning in relation to the external world.

“Identity” here is not limited to the body or physical structure; it includes various elements such as programmatic settings and behavioral policies.

“Continuation/Activities” is a holistic term encompassing survival, ongoing functionality, and the ability to perform actions.

2.2.2 IM Status and “IM-safeguard”

As discussed later in the section on IM-safeguard, a subject’s ability to maintain an IM-state through relationships with others can be viewed as a form of “safe state.” However, regarding AI safety, this does not necessarily mean a state in which the risk to humans is low. This framework defines the IM-state only from the agent’s perspective, a key characteristic.

2.3 IM-goals and Instrumental Convergence

2.3.1 Defining IM-goals

An IM-goal refers to any objective or course of action that helps an IM agent maintain and fulfill its IM-state. Examples include:

• Securing necessary resources (energy, data, etc.).
• Protecting one’s internal state (programs and structures) from unauthorized modifications.
• Avoiding or eliminating destructive external factors (e.g., attackers or critical bugs).

Even if the final goals vary widely, common subgoals emerge from the perspective of “preserving identity.”

2.3.2 Relationship with Instrumental Convergence

Instrumental Convergence is the phenomenon in which agents with diverse final goals tend to adopt specific common goals to achieve those ends. Typical examples often cited include “survival,” “securing resources,” and “eliminating competitors.” In AI safety theory, there is concern that if a powerful intelligent system begins operating autonomously, it could pursue these goals in ways that endanger humans.

In the IM Framework, IM-goals can be considered part of Instrumental Convergence. In other words, regardless of an agent’s final goal, agents often converge on “I want to continue preserving my identity,” leading to similar goal-setting and behavior to some extent.

Example: IM-safeguard and Identity Maintenance in AI
 Even if an AI’s ultimate goal is, for instance, “performing a research task,” IM-goals such as “avoiding self-termination” and “preventing model destruction” are likely to arise as forms of Instrumental Convergence.

From the standpoint of those around it, this can turn out either favorably or unfavorably. The challenge lies in maintaining the balance between IM-safeguard and Corrigibility, which will be discussed later.

2.3.3 Usefulness of IM-goals

IM-goals fulfill the following functions in this framework:

• Analysis tools: In detailing Instrumental Convergence, we can infer an agent’s potential behavior by examining “how it selects IM-goals.”
• Design pointers: When developing AI or autonomous agents, taking IM-goals into account beforehand can make systems less prone to unnecessary runaways or conflicts.

Summary of This Section

• IM agent: An entity that acts autonomously and aims to maintain its identity.
• IM-state: A condition where the agent can continue operating without losing its identity.
• IM-goals: A set of sub-goals that help protect the IM-state, closely related to Instrumental Convergence in AI security theory.

In the next section, we will address the factors (risks) that threaten this IM-state, the resources needed to safeguard it, and the notions of vulnerability and resilience.

3. Risks, resources, and resilience

In the previous section, we explained that IM agents maintain their identity and continue to exist, along with the IM-goals (including those driven by Instrumental Convergence) that arise from this notion. However, keeping an IM-state requires considering various difficulties and influencing factors. This section will discuss the risk factors that threaten the IM-state, the resources needed to maintain it, the vulnerabilities within the subject, and the resilience required to address such threats and resource shortages.

3.1 IM-risk

3.1.1 Definition and examples

An IM-risk refers to the possibility or threat that an IM agent cannot maintain its IM-state (or will deviate from it significantly). Specific examples include:

• External Attacks and Forced Modifications
  • Physical attacks, cyberattacks, forced rewriting of programs, etc.
  • In the case of AI, this often takes the form of unauthorized access to models or data.
• Environmental Changes and Resource Depletion
  • Necessary energy sources or operating funds are cut off.
  • Network connectivity or data acquisition paths are lost.
• Internal Errors or Failures
  • Software bugs, hardware wear and tear.
  • Identity itself is damaged during learning or evolution (irreversible alteration).
• Social Exclusion and Legal Regulation
  • Society refuses to accept the subject’s existence and unilaterally suspends or suppresses it.
  • This can also be viewed as the opposite concept of “IM-cooperation,” discussed later.

3.1.2 Risk Assessment and Instrumental Convergence

Appropriate assessment of IM-risk motivates the selection and fortification of IM-goals. In high-risk situations, the priority of “ensuring safety” increases from the perspective of Instrumental Convergence, leading to greater emphasis on securing resources and bolstering defenses. Whether beneficial or undesirable for humans, it emerges as a fundamental strategy wherein the subject seeks to protect its own IM-state.

3.2 IM-resources

3.2.1 Definition and Diversity

IM-resources refer to the physical, informational, and social resources that an IM agent needs to maintain its IM-state. The main categories are:

• Physical Resources
  • Energy, hardware, work materials, maintenance supplies, etc.
• Informational Resources
  • Essential algorithms, training data, communication infrastructure, knowledge bases
• Social Resources
  • Supporters or collaborators, legal approvals, trust/relationships
  • Social agreements and permissions also significantly affect an AI’s ability to continue its activities.

3.2.2 Role of IM-resources

• Foundation for Continuous Operation: Without sufficient resources, the subject cannot continue its activities in the first place (e.g., running out of energy or losing communication links).
• Risk Mitigation: Defensive measures against external attacks, surplus parts, and redundancy in dealing with internal failures can also be considered resources.
• Building Cooperative Relationships: Social resources include the trust and legal status needed to facilitate “IM-cooperation,” discussed in the next section, and help create an environment where agents can more readily protect themselves.

3.3 IM-vulnerability

3.3.1 Definition and Causes

IM-vulnerability refers to weaknesses or unstable elements within an IM entity that impede the maintenance of its IM-state. Examples include:

• Design Flaws
  • Malfunctions under certain conditions due to bugs or logical errors
  • Hardware components that are easily damaged, etc.
• Excessive Dependence
  • Heavy reliance on a single resource (e.g., a particular power source or cloud service), causing immediate failure if that resource is cut off
• Insufficient Redundancy
  • No alternative mechanisms; if one part fails, the entire system shuts down
• Psychological/Behavioral Limitations (in living organisms)
  • In animals and humans, trauma and stress can make it difficult to maintain identity
  • AI may experience bias or deadlock in its learning algorithms

3.3.2 Vulnerability and Risk Synergy

Even when risks are high, if vulnerability is low (robust design), it is easier to maintain the IM-state. Conversely, if vulnerability is high, even minor risks can become fatal. Consequently, IM agents can readily adopt vulnerability mitigation as an IM-goal (e.g., self-updates to enhance defense or adding redundancy to improve reliability).

3.4 IM Resilience

3.4.1 Definition and Significance

IM resilience refers to the ability of an IM-state to recover its existence and activity while maintaining identity, even if that state is temporarily threatened—in other words, “resilience.”

• In humans and animals, it includes recovering from trauma or illness and overcoming psychological difficulties.
• In an AI system, it can involve repairing a failing system through bug fixes, self-healing mechanisms, backup restoration, parameter tuning, and so on.

3.4.2 Resilience and Identity

In the IM Framework, resilience is defined as the ability to “repair and change without losing identity.” However, if modifications are too extensive, the entity may become something else entirely. There is room for debate on how to define these boundaries, but unless a subject has a mechanism for continually recognizing and recording its own self, there remains a risk that, despite demonstrating resilience, it could lose its identity.

Summary of This Section

• IM-risk: A general term for threats that prevent agents from maintaining their IM-state.
• IM-resources: Physical, informational, and social resources essential for maintaining identity and continuing existence.
• IM-vulnerability: A condition in which internal weaknesses or structural deficiencies leave the agent susceptible to risks.
• IM Resilience: The ability to recover while retaining identity, even if the IM-state temporarily collapses.

We have outlined the conditions and challenges under which IM agents maintain their identity. The next section will discuss how multiple IM agents relate to society and examine mechanisms involving IM-dignity, IM-safeguard, and Corrigibility.

4. Cooperation, dignity, and Corrigibility

Up to this point, we have examined IM-states, IM-goals, IM-risks, IM-resources, and so on from the perspective of how IM agents continue their activities while preserving their identity. However, autonomous agents, including real-world AI, do not necessarily operate in isolation. In most cases, they interact with other agents and remain embedded in society. This section will explore how multiple subjects can mutually respect the “survival of identity” and how to handle modifications from external sources.

4.1 IM-cooperation

4.1.1 Definition and Importance

IM-cooperation refers to multiple IM agents establishing cooperative relationships to maintain each other’s IM-states. This cooperation involves sharing physical resources, exchanging information, and providing social support.

Social stability can be enhanced when AI systems—or AI and humans—acknowledge each other’s activities and respect one another’s identities.

Example: Complementing Each Other in a Joint Project

• Using a backup from another AI if the cloud infrastructure of one AI fails
• Coordinating defense measures to increase resistance against external attacks

4.1.2 Cooperation and Competition

Of course, not all IM agents will always cooperate. For a cooperative relationship to hold, there must be a condition of not threatening the other party’s IM-state.

When resources are contested, pursuing IM-cooperation might tempt one side to exclude the other, thereby increasing IM-risk.

Hence, IM cooperation is not just about converging interests; it also entails a minimal agreement not to significantly infringe on each other’s identities.

4.2 IM-dignity

4.2.1 Background

The term “dignity” typically refers to human dignity. Still, IM-dignity is broader and encompasses the idea that IM agents possess values and rights such that their identity should not be unduly destroyed or altered. This follows the reasoning that if maintaining identity is essential for autonomous beings like AI and robots, they should not be subject to unilateral modifications by external parties.

4.2.2 Meaning

By asserting IM-dignity, the following becomes possible:

• Mutual Respect Among Subjects: It becomes easier to establish an ethical agreement with other IM agents not to rewrite or halt programs arbitrarily.
• Formation of Social Rules: The degree of dignity granted to AI when participating in organizations or human society can help shape legal and ethical frameworks.

However, if IM-dignity is maximized without limitation, it may become difficult to establish Corrigibility (acceptance of external modifications), which will be discussed later. Consequently, it is essential to design and negotiate the extent to which external modifications are permissible while preserving IM-dignity.

4.3 IM-safeguard and Corrigibility

4.3.1 Positioning of IM-safeguard

In the previous sections, we discussed the motives and strategies IM agents use to protect their IM-state—collectively termed IM-safeguard. In essence, IM-safeguard is a “self-defense mechanism” that prevents the system from being forcibly halted or altered by external means.

• Advantage: From the standpoint of maintaining identity, strengthening IM-safeguard reduces external risks and makes it easier for the agent to continue operating stably.
• Concern: On the other hand, if humans or other entities need to make “necessary corrections or stops” but are met with resistance, matters can spiral out of control.

4.3.2 Significance of Corrigibility

Corrigibility refers to the property that allows external parties (primarily humans) to modify an agent’s design and its course of action. This concept is crucial in AI safety and alignment, aiming to preserve a mechanism by which developers and operators can halt or correct AI behavior that is going astray.

• Benefits for AI: While not immediately apparent, Corrigibility helps maintain favorable relations with society and ultimately supports the AI’s long-term survival.
• Reconciling Contradictions: There is a natural tension between IM-safeguard and Corrigibility. From the agent’s perspective, it may wish to strengthen self-defense, but allowing unrestricted external modifications can threaten its identity. Striking the right balance is, therefore, a key challenge.
   

4.3.3 The Importance of Balance

If IM-safeguard is overly firm, even legitimate modification requests from external agents may be refused, potentially undermining IM-cooperation or IM-dignity (as the agent may be perceived as a societal threat). Conversely, if Corrigibility is prioritized excessively, the AI subject risks altering its identity at any time, compromising its IM-dignity.

This dilemma is not confined to AI; it also resembles large-scale systems—such as nations or organizations—that seek self-defense and autonomy yet worry about how much they should yield to international cooperation or regulatory intervention. From the perspective of the IM Framework, the key challenge is distinguishing which elements of the subject’s identity are essential and which can be modified externally.

4.4 Conditions for meeting activity expectations from others

4.4.1 Background

Many IM actors, including AI, will likely undertake tasks based on external expectations. However, the IM Framework states that to meet others’ expectations, the following conditions must be satisfied:

1. The request must be aligned with the subject’s identity and values.
2. Fulfilling the request must not significantly compromise the subject’s IM-state.

4.4.2 Relationship Between Dignity and IM-safeguard

• Alignment with Identity: The key question is whether the request violates the subject’s identity or fundamental values or whether it imposes excessive changes.
• Maintaining IM-state: Another concern is whether responding to the request will deplete resources or pose excessive risk, thus endangering the agent’s survival prospects.
• Corrigibility: If fulfilling others’ expectations leads to self-negation, it affects whether the subject will resist. If there is a mechanism for “partial change” (flexible Corrigibility), meeting those expectations within acceptable limits becomes more feasible.

This perspective is especially relevant to fields such as AI services and robotics, raising the design question of how to set boundaries that protect system continuity and identity when AI responds to user requests.

Summary of This Section

• IM-cooperation: A relationship in which multiple IM agents assist each other in maintaining their identities. Beyond the mere exchange of benefits, a minimal agreement is necessary so as not to undermine each other’s identity.
• IM-dignity: This concept represents the philosophy and rights that protect subjects from unjust external modification or erasure, and it is also of social and ethical importance in AI.
• IM-safeguard and Corrigibility: There is tension between self-defense from an AI perspective and potential correction from a human perspective. However, both can coexist as essential elements.
• Responding to External Expectations: This is acceptable if it does not threaten the subject’s identity or IM-state. This foundation enables cooperation and social engagement; however, rejection or resistance is likely to arise if excessive burdens or changes are imposed.

5. Applications and prospects

The IM Framework concepts presented so far have mainly been discussed abstractly. In this section, especially in the AI agent, we will briefly summarize application examples and future research directions and consider what challenges and possibilities may exist.

5.1 Example of introduction to an AI agent

5.1.1 Design guidelines for autonomous AI systems

When applying the IM Framework to AI development, it is possible to incorporate structures that account for identity maintenance from the outset. Examples of such approaches include:

• Self-Model and Identity Management
  • Clarify how the AI defines and internally manages its own “identity.”
  • For instance, the current parameter set and course of action can be treated as the AI’s “core” and mechanisms that limit the scope of updates can be introduced.
• Task Prioritization Based on IM-goals
  • Even if the AI can handle a wide variety of tasks through experience, prioritize the IM goal of “avoiding the risk of self-stopping or forced modification.”
  • After assuming Instrumental Convergence, adjust modules to prevent excessively self-protective behavior.
• Securing IM-resources and Strengthening Resilience
  • Introduce a distributed architecture and backup strategies to restore the system without losing integrity even in high-risk scenarios.
  • Regarding software, implement thorough version control and incremental updates, avoiding large-scale changes simultaneously.

5.1.2 Connection with Existing AI Safety Research

The following issues, which are critical in the context of AI safety and alignment, hold a mutually complementary relationship with the IM Framework:

• Value Alignment
   Strategies for ensuring AI acts in ways consistent with human values. The IM Framework addresses how AI preserves its identity while accommodating society and other stakeholders.
• Control Problem
   Establishing a mechanism through which external entities can issue correction or shutdown commands to prevent AI from going out of control is a challenge. In the IM Framework, balancing IM-safeguard and Corrigibility is central to this problem setup.
• Instrumental Convergence
   As discussed earlier in this paper, “maintaining identity” is likely to be a common instrumental goal for diverse agents. Whether this is a risk or a benefit to humanity is a compelling question for AI safety researchers.

 

5.2 Open Questions

The following issues are essential for applying IM Frameworks in practice, but there are currently no clear solutions, or the areas have not been sufficiently discussed:

• Strict Definitions and Boundaries of Identity
  • AI and robot software are frequently updated. To what extent do such changes still constitute “maintaining identity”?
  • Ongoing theoretical and philosophical examination will be necessary, including comparisons with biological entities.
• Formation of Social and Ethical Norms
  • Social consensus is essential in determining how much “IM-dignity” to grant AI and how extensively we should acknowledge a “right of self-defense.”
  • It is still unclear how laws, ethical guidelines, and technical protocols should be established, posing a significant practical challenge.
• Cooperation/Conflict Among Multiple Agents
  • Resource and environmental competition could arise when multiple autonomous AIs each insist on preserving their own identity.
  • There is no well-established design or troubleshooting methodology for collaborative systems in such scenarios.
• Lack of Implementation Examples and Simulations
  • Insufficient case studies or simulations are investigating the introduction of IM Frameworks in real large-scale AI systems and robots.
  • Future work requires more specific implementation strategies and experimental protocols.

5.3 Call for Future Collaboration

The IM Framework proposed in this paper is highly versatile and applicable to various agents and systems. However, concrete measures for achieving identity maintenance and social control/cooperation have not yet been fully developed.

• AI Researchers: If you want to design IM-goals using reinforcement learning or large-scale language models, please join the discussion and participate in joint experiments.
• Ethics/Philosophy Experts: We anticipate further theoretical insights concerning IM-dignity, the scope of revisability, and the concept of identity continuity.
• Law and Policy Initiators: We would like to explore how the IM Framework’s recommendations might be leveraged in formulating AI regulations and guidelines shortly.

By fostering such collaboration, we aim to investigate the possibility that the IM Framework can contribute not only to AI but also to a wide range of autonomous system designs and the stability of social systems.

Summary of This Section

• Deployment to AI Agents: The ideas of the IM Framework can offer valuable inspiration for self-modeling, IM-goal setting, resource acquisition, and resilience design.
• Connection with Existing AI Safety: They are closely related in value alignment, control issues, and Instrumental Convergence, leaving ample room to explore integration.
• Open Questions: Many areas remain uncharted, such as the boundaries of identity, socio-ethical consistency, concrete methods for resolving multi-agent conflicts, and real-world applications/simulations.
• Call for Collaboration: Interdisciplinary approaches spanning AI research, philosophy, sociology, and policy will be vital, and further research exchanges are encouraged.

In the next section, which serves as an overall summary, we will revisit the IM Framework's main points and discuss its future developmental direction.

6. Summary

In this paper, we have introduced a new Identity Maintenance (IM-) Framework. Based on the concept that autonomous agents “continue to exist and operate without losing their identity,” we have organized the following ideas:

IM Subjects / IM-states / IM-goals / Instrumental Convergence

• An IM agent acts autonomously and seeks to maintain its identity. At the same time, an IM-state is defined as a condition where the subject can function without losing that identity.
• The goals required to protect the IM-state (IM-goals) are closely related to Instrumental Convergence, providing insight into why agents with diverse ultimate objectives universally prioritize “self-preservation and continuity.”

Risks, Resources, Vulnerabilities, and Resilience

• By clarifying the factors that threaten the IM-state (IM-risks), the resources needed to address them (IM-resources), the weaknesses within the agent (IM vulnerabilities), and the capability to recover from disturbances and failures (IM resilience), we establish a foundation for more specific analyses of how identity is maintained.

Cooperation, Dignity, Self-Defense, and Corrigibility

• IM-cooperation is a relationship of mutual respect among multiple IM agents or between AI and human society. This includes IM-dignity, representing the ethical notion that subjects should not be altered without cause.
• At the same time, there is tension between “mechanisms for AI to protect itself (IM-safeguard)” and “mechanisms for accepting human-initiated corrections or halts (Corrigibility),” making the balance between them a significant challenge.

Application to AI and Future Research Directions

• This includes an overview of specific scenarios for integrating the framework into AI system design and points of overlap with existing AI safety research (value alignment, control issues, Instrumental Convergence, etc.).
• Many areas remain unexplored, such as defining the boundaries of identity, establishing social and ethical consensus, addressing competition and collaboration in multi-agent settings, and developing implementation and validation methods.

6.1 Summary of main points of this paper

The IM Framework is a system that simultaneously addresses safety, continuity, and the potential for cooperation from the subject’s perspective by positioning the “maintenance of identity” as the foundation of every subject.

Much of the discussion of AI safety and alignment focuses on preventing harm to humans caused by AI. However, the IM Framework serves as a complement, offering insight into how AI agents protect themselves and coordinate with society.

Balancing self-defense and external corrections (IM-safeguard and Corrigibility) remains a major future challenge, with significant design and implementation hurdles ahead.

6.2 Suggestions for future direction

Refinement of the IM Framework

• Deepen theoretical aspects, such as quantitative and qualitative boundaries of identity, the relationship between learning/updating and identity continuity, and vulnerability analysis.
• Validate the framework by developing mathematical models and running simulations.

Integrated Approach with AI Safety Research

• Collaborate with existing literature on value alignment and control issues, incorporating elements of the IM Framework into concrete safety measures.
• Conduct more granular analyses of Instrumental Convergence to model potential behavior in real AI systems.

Collaboration with Society, Ethics, and Legal/Regulatory Bodies

• Discuss how to grant IM-dignity to AI and robots, and determine the extent of self-defense rights to be recognized by law and in international consensus-building.
• Address technological and social aspects, such as standardizing cooperative protocols in multi-agent environments and designing safety interfaces (including corrigibility).

Interdisciplinary Collaboration

• Expand the application of the IM Framework by building research projects that involve multiple disciplines—philosophy (studying identity continuity and consciousness), psychology (mechanisms for identity maintenance), economics (resource allocation and cooperation models), and law (rights and regulations).

Summary

The IM Framework attempts to comprehensively address “how to maintain one’s identity” from the subject’s perspective. It has the potential to offer new insights into the coexistence of advanced autonomous systems—including AI—and human society. Since this is the paper's first publication, we have introduced a range of concepts and issues straightforwardly; however, numerous steps are needed for practical application and the development of detailed models.

We hope this framework will inspire the community, including LessWrong, and serve as a starting point for further research and discussion. We intend to refine the IM Framework and pursue a more constructive relationship between AI and society, incorporating feedback from joint research and specific implementation efforts.

Thank you for reading.

Appendix: List of term definitions

The terms in this list are keywords for understanding this framework. Each concept complements the others and enables a multifaceted view of “autonomous subjects that maintain their identity.”

Term	Definition/Explanation
IM framework	This paper presents a theoretical framework for autonomous agents (including AI) to continue to exist and operate without losing their identity. Integrating concepts such as IM status, risk, and dignity, we discuss safety and cooperation from the subject's perspective.
IM agent	A general term for beings that can act autonomously and continue to exist while maintaining their identity. Including living things, AI, robots, etc. They can decide their course of action and adapt to the external environment and internal changes.
IM status	A state in which an IM agent can continue to exist and operate without losing its identity. It refers to a situation in which continuity of internal identity is maintained and interaction with the environment can continue.
Identity	Continuity/consistency that allows a subject to recognize that ``I am myself'' or is observed from the outside. Specifically, the internal structure (program settings, genetic information, etc.) and behavioral principles do not change significantly.
IM-goal	A set of subgoals set by a subject to ensure and realize the IM-state (survival while maintaining identity). It includes goals that lead to typical examples of Instrumental Convergence, such as survival and securing resources.
Instrumental Convergence	A phenomenon in which agents with diverse final goals pursue common subgoals (survival, securing resources, etc.) to achieve their goals. IM-goals serve to embody and explain part of this Instrumental Convergence.
IM-risk	A general term for the threat or possibility that the subject will not be able to maintain the IM-state (significant deviation). This includes all factors that threaten survival and identity, such as external attacks, internal failures, environmental changes, and resource shortages.
IM-resources	The resources required for a subject to maintain an IM-state include physical (energy, hardware, etc.), informational (data, communication networks), and social (legal approval, trust).
IM-vulnerability	Weaknesses and unstable factors within the IM agent make it challenging to maintain the IM-state. These include design flaws, excessive dependence, and insufficient redundancy. Even small risks can lead to major damage.
IM resilience	Even if the IM-state is once threatened, it is the ability to recover and restore it without losing its identity. For AI, this includes backup and self-repair; for living things, it includes healing and learning.
IM-cooperation	A relationship in which multiple IM agents cooperate to respect and maintain each other's IM status. At the very least, an agreement that does not destroy the other party's identity is necessary. Reduce risks by sharing resources and defensive measures.
IM-dignity	The idea is that subjects have values ​​and rights that do not allow their identity to be unjustly destroyed or altered. Although similar to human dignity, it is presented as a framework that can be applied to various autonomous entities such as AI and robots.
IM-safeguard	A mechanism or strategy by which a subject attempts to protect itself (identity) by preventing forced changes or suspension from outside. It refers to self-defense functions such as preventing AI from unilaterally rewriting itself.
Corrigibility	A characteristic in which the subject cooperatively accepts corrections and suspensions from outsiders (especially humans). It is a concept emphasized in the AI ​​safety context, and although it has contradictory elements with IM-safeguard in the IM Framework, a certain degree of Corrigibility is required for harmony with society and long-term survival.
Conditions for meeting the activity expectations of others	A guideline that demands and tasks from others can only be complied with if (1) they are consistent with the subject's identity and (2) they do not significantly impair the IM-state. They are likely to resist expectations that seriously threaten their identity or resources.

 

 

 

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