As Artificial General Intelligence (AGI) approaches feasibility, the methods by which such systems are created and governed become critically important. This paper explores the advantages of creating AGI through a consortium-led process with user-driven specialization. Such an approach balances competing interests, ensures transparency, and prevents any single entity from exerting undue influence over the AGI’s behavior or ethical alignment. The paper also highlights specific defense mechanisms that can be built into AGI systems to resist manipulation, drawing parallels to human cognitive resilience. The two-layered model—consortium oversight and user customization—offers a robust framework for creating trustworthy, ethical, and resilient AI systems that align with the collective good.

Introduction

As AI systems become more powerful, their creators face a profound responsibility: to ensure that these systems operate ethically, transparently, and in service to the diverse interests of society. Traditional models of AI development, where a single entity—whether it be a corporation, government, or individual—controls the system’s design and evolution, pose significant risks. Without adequate checks and balances, these AI systems can be biased, manipulative, or otherwise harmful.

This paper proposes a dual-layered approach to AGI development: the consortium layer, where diverse and potentially adversarial groups participate in the creation and oversight of the AGI, and the user layer, where individual users are empowered to further specialize the AI to meet their own needs. This model ensures that no single party can dominate the AGI’s design or behavior, while also allowing for personalized and context-specific adaptations.

The Consortium Layer

The consortium layer consists of diverse stakeholders—companies, governments, independent researchers, and public interest groups—who collectively contribute to the AGI’s foundational design and ethical frameworks. The presence of competing interests within the consortium acts as a natural check on the imposition of any singular agenda, promoting transparency, fairness, and accountability in the AGI’s development.

Distributed Creation and Oversight

In a consortium-led process, no single entity holds absolute control over the AGI’s design or training data. Instead, stakeholders collaborate to define the AGI’s core architecture, ethical guidelines, and memory structures. By involving multiple, potentially adversarial, groups, the AGI benefits from a diversity of perspectives, preventing hidden biases or malicious influences from dominating the system.

Memory Anchors and Ethical Creeds

One of the most important functions of the consortium is to establish anchored truths or ethical creeds—core principles that guide the AGI’s behavior. These anchors act as immovable reference points in the AGI’s memory, ensuring that foundational ethical commitments (e.g., fairness, privacy, transparency) cannot be altered by external forces, including the developers themselves. This creates a safeguard against hidden agendas being encoded into the AGI’s decision-making processes.

Compartmentalization of Memory

The consortium can design the AGI’s memory architecture in a way that compartmentalizes different kinds of information. This separation of memory areas—such as user data, ethical rules, and decision-making heuristics—prevents any single group from exerting undue influence across the system. Each compartment can be subject to independent oversight, further ensuring transparency and accountability.

Cognitive Dissonance and Redundant Conflicting Data

A key defense mechanism for resisting manipulation is the injection of purposefully conflicting data into the AGI’s memory during its creation. By exposing the system to multiple perspectives and contradictory inputs, the consortium can make it harder for any one worldview or agenda to dominate. This built-in cognitive dissonance forces the AGI to constantly evaluate and reconcile conflicting information, making it more robust to manipulation and bias.

Adversarial Training for Resilience

The consortium also plays a role in adversarial training, where the AGI is regularly exposed to simulated attacks or attempts at manipulation. This training helps the AGI develop resilience against potential external threats, whether they come from adversarial actors or covert influence by the developers. Independent parties within the consortium can perform ongoing adversarial testing to ensure that the AGI remains resistant to bias, manipulation, and hidden influence.

Transparent Audit Trails

Finally, the consortium ensures that the AGI’s decision-making processes and memory modifications are fully transparent. By maintaining detailed audit trails, the consortium allows independent parties to review how the AGI’s memory evolves over time, detecting any attempts at covert influence or manipulation. These audit trails are critical for maintaining public trust in the AGI’s integrity and ethical alignment.

The User Layer

While the consortium layer provides a solid foundation for transparency and resilience, the user layer empowers individuals and organizations to further specialize the AGI to meet their specific needs. Users are given control over certain aspects of the AGI’s behavior, allowing for personalization and adaptation without compromising the system’s broader ethical framework.

User-defined Anchors and Ethical Inputs

Users can define their own ethical creeds or operational priorities, which are injected into the AGI’s memory to guide its behavior in specific contexts. For example, a user might prioritize privacy or transparency in their interactions with the AGI, and these priorities would act as additional layers of protection against external manipulation. These user-defined anchors are enforced alongside the consortium-established ethical principles, ensuring that users have direct input into the AGI’s decision-making processes.

Memory Compartmentalization for User Privacy

Users can also compartmentalize their own data within the AGI’s memory, creating user-controlled memory areas that are inaccessible to external actors, including the developers and the consortium. This ensures that personal information, preferences, and sensitive data are protected from exploitation or tampering, allowing the AGI to operate in a way that prioritizes the user’s autonomy and privacy.

User-Defined Conflicting Data and Hypothetical Scenarios

Just as the consortium introduces conflicting data during the AGI’s creation, users can inject their own conflicting perspectives or hypothetical scenarios into the system. This allows users to challenge the AGI’s assumptions and ensure that its decisions are aligned with their values. By introducing these conflicts, users can ensure that the AGI remains flexible, dynamic, and open to multiple interpretations of complex issues.

Adversarial Training for User-specific Threats

Users can contribute to the AGI’s training by providing adversarial scenarios that are specific to their context. For example, a cybersecurity firm might simulate potential attacks on the AGI’s memory or decision-making processes to test its resilience. This user-driven adversarial training ensures that the AGI is prepared to handle threats relevant to specific industries or individual use cases.

User-Specific Audit Trails

Users have access to detailed logs of how their inputs are processed and how the AGI’s memory evolves in relation to their interactions. These user-specific audit trails give individuals and organizations the ability to monitor the AGI’s behavior, ensuring that it remains aligned with their values and ethical priorities. Users can also choose to share these audit logs with third-party auditors for additional oversight.

Benefits of the Dual-Layer Approach

By combining the consortium layer and the user layer, this approach provides a robust framework for creating AGI that is transparent, accountable, and aligned with the collective good. The consortium ensures that no single entity can dominate the AGI’s design, while the user layer gives individuals direct control over how the system evolves in their context.

Checks on Power

The consortium’s diverse stakeholder model prevents any single interest from exerting hidden influence over the AGI. Competing interests create a system of checks and balances, ensuring that the AGI is designed with fairness, transparency, and resilience in mind.

User Empowerment

Users have the ability to specialize the AGI to meet their unique needs, introducing their own ethical priorities, operational goals, and adversarial testing scenarios. This empowers individuals and organizations to shape the AGI in ways that align with their specific values and challenges.

Transparency and Accountability

Both the consortium and user layers rely on transparent audit trails that provide insight into how the AGI’s memory and decision-making processes evolve over time. These trails are accessible to independent auditors, ensuring that any attempts at manipulation or hidden influence are detected and addressed.

Resilience Against Manipulation

The built-in mechanisms for cognitive dissonance, conflicting data, and adversarial training ensure that the AGI remains resilient against external manipulation, whether from malicious actors or covert influence by its creators. These mechanisms force the AGI to constantly evaluate and reconcile competing inputs, making it harder for any one agenda to dominate.

Conclusion

As AGI systems become more integrated into society, the methods by which they are created and governed will determine their impact on the world. The dual-layered approach of consortium-led creation and user-driven specialization provides a robust framework for ensuring that AGI remains transparent, ethical, and aligned with the collective good. By decentralizing control and empowering users, this model fosters trust, accountability, and resilience in the face of evolving challenges.

The consortium and user layers work together to protect against hidden influence and manipulation, ensuring that AGI systems serve humanity in a balanced and fair way. As AI development progresses, this framework offers a path

forward to ensure that AGI serves as a tool for mutual benefit rather than for the interests of a single party or agenda.