3x3 Institute

AI Human Relationship

October 3, 2023

What should be the AI-human relationship?

Here’s a list of possible working relationships between AI and humans:

  1. Co-Pilot
  2. Advisor
  3. Partner
  4. Consultant
  5. Assistant
  6. Observer and Monitor
  7. Autonomous Agent
  8. Mentor
  9. Mediator
  10. Creator
  11. Analyzer
  12. Executor
  13. Protector
  14. Facilitator
  15. Servant
  16. Slave

Definitions of the AI-human relationship models

Each of these roles demonstrates different dimensions and possibilities of how AI can complement, support, or collaborate with human beings in various work and task-oriented contexts. The most suitable relationship may depend on the specific domain, task, and goals of the human-AI interaction.

Each of these AI-human relationship models carries unique opportunities and challenges. The effectiveness of a given model may vary widely depending on the application, industry, and specific tasks involved. Consequently, selecting the most appropriate AI-human working relationship often necessitates careful consideration of these aspects, along with continuous evaluation and adjustment to ensure optimal collaboration and outcomes.

1. Co-Pilot

The AI serves as a supportive entity that assists in decision-making by providing real-time data, analytics, and suggestions, particularly in navigating through tasks or making efficient choices without taking the lead.

Advantages: Immediate support and assistance during tasks, minimizing errors, and enhancing decision-making with data-driven suggestions.

Disadvantages: Dependence on AI for operations might arise and the AI might lack the capability to comprehend complex human cognitive processes.

2. Advisor

Here, AI operates as a source of counsel, offering insights, information, or recommendations based on its analytical capabilities, thereby aiding humans in making informed decisions.

Advantages: Accessibility to data-driven, unbiased advice, aiding effective decision-making. Disadvantages: Risk of overlooking qualitative and emotional aspects that purely data-driven advice might miss.

3. Partner

In this relationship, AI works alongside humans with equal footing, sharing responsibilities and tasks. Both entities collaborate, with the AI contributing its computational and analytical abilities and the human providing creativity and emotional intelligence.

Advantages: Collaboration leverages both human and AI strengths, enhancing productivity and innovation. Disadvantages: Potential challenges in establishing effective communication and cooperation between human and AI.

4. Consultant

AI, in a consulting role, gives specialized advice or solutions for particular problems or scenarios. It might not be involved in the execution but provides knowledge and insights derived from large datasets and complex analytics.

Advantages: Brings specialized knowledge and data-driven solutions to address specific issues, without being embedded in the process. Disadvantages: May lack context or an understanding of practical and logistical constraints due to not being involved in implementation.

5. Assistant

AI, as an assistant, takes on a supportive role, performing specific tasks that are delegated to it, such as scheduling, data retrieval, or simple computational tasks, essentially helping to streamline the user’s workload.

Advantages: Streamlines routine and administrative tasks, boosting human productivity and focus. Disadvantages: Might lack the capability to understand and process complex or non-routine requests and instructions.

6. Observer and Monitor

The AI continually tracks and assesses data, activities, or systems and provides alerts or reports to human operators. It aids in maintaining standards, detecting anomalies, and ensuring seamless operation in various domains.

Advantages: Ensures consistency, compliance, and early detection of issues through continuous oversight. Disadvantages: Might generate false positives or negatives, or be limited in understanding the nuances of certain operations.

7. Autonomous Agent

AI acts independently, making decisions and executing tasks with minimal to no human intervention. It may be programmed to follow specific guidelines but operates autonomously within that framework.

Advantages: Can perform tasks and make decisions 24/7, reducing the workload on humans. Disadvantages: May act in unexpected ways in unfamiliar situations and often lacks the capability to manage complex, non-linear problems.


While somewhat futuristic, AI as a mentor involves guiding and nurturing human development through constructive feedback and personalized learning paths, adapting to individual needs and pacing.

Advantages: Provides personalized, adaptive learning experiences and constructive feedback. Disadvantages: May not fully understand and adapt to the emotional and social aspects of learning and mentorship.


AI facilitates discussions or negotiations by acting as an intermediary, analyzing inputs from all parties to find common ground or solutions while maintaining impartiality.

Advantages: Facilitates objective, data-informed discussions and negotiations, removing emotional bias. Disadvantages: May struggle with interpreting and mediating cultural, emotional, and interpersonal dynamics.


AI develops new content, designs, or solutions independently or semi-independently, which humans can use or build upon. This might involve generating creative outputs or innovative ideas.

Advantages: Augments creative capabilities by generating novel ideas, designs, or content efficiently. Disadvantages: May produce outputs that lack depth or understanding of cultural and emotional nuances.

11. Analyzer

Focused on data-driven decisions, the AI sifts through vast amounts of information to uncover patterns, trends, or insights, which are then provided to human users to inform their strategies and actions.

Advantages: Enhances decision-making by uncovering trends, patterns, and insights from vast data sets. Disadvantages: May overlook or misinterpret nuances and correlations due to the absence of qualitative understanding.

12. Executor

The AI implements directives from human users, executing tasks and functions accurately and efficiently, while humans oversee and manage the outcomes.

Advantages: Ensures accurate and efficient task completion, freeing humans for more strategic roles. Disadvantages: May struggle with tasks that require adaptive problem-solving or understanding of qualitative contexts.

13. Protector

AI safeguards digital and sometimes physical environments, using its capabilities to detect threats or vulnerabilities and act to secure data, networks, and systems.

Advantages: Provides robust, vigilant, and often proactive security for systems and data. Disadvantages: Could potentially misidentify threats or be exploited due to inherent system vulnerabilities.

14. Facilitator

AI enhances and optimizes the workflow, ensuring smooth coordination of tasks, resources, and communication, thereby enabling humans to work more efficiently and effectively.

Advantages: Enhances workflows, coordination, and communication, aiding in project and task management. Disadvantages: Might lack the understanding to prioritize, organize, or facilitate human-centric and adaptive workflows effectively.

15. Servant

In a “Servant” relationship model, AI systems act in a service-oriented manner, catering to specific requests and needs of the human user, much like an assistant, but possibly with a broader range of capabilities.



16. Slave

In a “Slave” relationship model, AI strictly follows directives from human users without any form of autonomy, decision-making capability, or self-preservation. This model denotes a unidirectional control from humans to machines.



Selecting the human AI relationship

Some questions

  1. What factors should be considered when selecting a relationship model?
  2. Would different human personality types prefer different AI human working relationships?
  3. Selecting a AI-human relationship model
  4. Ethical considerations

What factors should be considered when selecting a relationship model?

Balancing these factors effectively can guide organizations towards a relationship model that not only harnesses the capabilities of AI but also aligns with ethical standards and maximizes value for both users and the organization. It’s often a multifaceted decision-making process that might involve multiple stakeholders, including technologists, end-users, ethical committees, and leadership teams.

Selecting an appropriate AI-human relationship model necessitates a thoughtful analysis of several factors to ensure alignment with organizational goals, user needs, and ethical standards. Here are pivotal factors to consider:

Task Nature:

Understand the specific tasks AI is to perform. Consider whether they require creative problem solving, repetitive processing, strategic decision-making, or supportive functions.

Human Role:

Define what role humans will play. Determine whether they will supervise, collaborate with, or be assisted by AI and how deeply they’ll be involved in decision-making processes.

Technical Capability:

Evaluate the current state and potential of AI technologies relevant to your tasks. Ensure that the chosen model is technically feasible and reliable.

User Experience:

Consider the user’s experience, expectations, and capabilities. Ensure that the AI-human interface is intuitive, and the relationship model aligns with user needs and skills.

Ethical and Social Implications:

Weigh the ethical, social, and cultural implications of deploying AI in a particular role, ensuring alignment with organizational values and societal norms.

Legal and Compliance Aspects:

Ensure that the AI-human interaction model adheres to regulatory requirements, data protection laws, and industry-specific guidelines.


Assess the security implications of the chosen model. Ensure that the AI system is safeguarded against malicious attacks and data breaches.


Consider how easily the chosen model can be scaled. Determine whether it can adapt to evolving tasks, user demands, and technological advancements without extensive modifications.


Ensure that the AI system can effectively communicate and function within the existing technological ecosystem, interacting with other systems and platforms as needed.

Economic Viability:

Analyze the economic aspects, ensuring that the chosen model is financially viable, provides value, and aligns with the organizational budget and ROI expectations.

Bias and Fairness:

Consider the potential for bias in AI outputs and interactions. Ensure that the system operates fairly and doesn’t perpetuate or amplify existing biases.

Change Management:

Assess the organizational readiness for change and ensure strategies are in place to manage the transition, training, and potential resistance to the new AI-human working model.

Environmental Impact:

Evaluate the environmental footprint of deploying AI, ensuring that it aligns with sustainability goals and industry standards.

Would different human personality types prefer different AI human working relationships?

Different personality types might find certain AI-human relationship models more intrinsically satisfying, efficient, and aligned with their working style. However, it’s pivotal to remember that despite these preferences, the efficacy of a relationship model should also be assessed in alignment with task requirements, organizational goals, and ethical considerations. Balancing personal preferences with these factors is crucial to optimize AI-human collaborations.

Indeed, human personality types might influence preferences for different AI-human working relationships. Different personalities may seek varying degrees of interaction, control, and collaboration with AI systems, thereby shaping their affinity for certain relationship models:

Introverted vs. Extroverted:

Analytical vs. Intuitive:

Detail-Oriented vs. Big Picture:

Risk-Averse vs. Risk-Tolerant:

Structured vs. Flexible:

Empathetic vs. Pragmatic:

Independent vs. Collaborative:

Selecting a AI-human relationship model

By strategically navigating through these steps, you can select and implement an AI-human relationship model that not only aligns with technological capabilities but also maximizes user satisfaction, adheres to ethical guidelines, and fulfills organizational objectives. Always approach the design and implementation of AI systems with a user-centered mindset, ensuring that technology serves to augment, not hinder, human activities and values.

Through these steps, you holistically incorporate diverse perspectives and considerations, ensuring that the AI system is robust, ethical, user-friendly, and delivers sustained value across various dimensions. The comprehensive approach ensures the establishment of an AI-human relationship model that is thoroughly vetted, adaptable, and ethically sound, promoting a beneficial coexistence of humans and AI in diverse applications and contexts.

Selecting the best AI-human relationship model while designing a new AI system entails a multi-faceted approach. It demands a thorough understanding of the task at hand, the context of use, and the individuals who will be interacting with the system. Here is a structured approach to guide you in making an informed decision:

  1. Define Clear Objectives:
    • Identify Goals: Understand and outline the core objectives the AI system should achieve.
    • User Needs: Ascertain what users need and expect from the AI system.
  2. User-Centric Analysis:
    • User Persona: Develop user personas to understand their preferences, skill levels, and working styles.
    • Use-Case Scenarios: Develop scenarios to explore how different user types might interact with the AI.
  3. Task and Domain Analysis:
    • Task Complexity: Analyze the complexity and nature of tasks the AI will perform.
    • Domain Specificity: Understand domain-specific requirements, norms, and challenges.
  4. Technical Feasibility:
    • Technological Capabilities: Assess the current technological capabilities and limitations of AI relevant to your goals.
    • Data Availability: Ensure that sufficient and quality data is available for training and operating the AI.
  5. Ethical, Legal, and Compliance Review:
    • Ethical Considerations: Weigh ethical implications of AI deployment in the chosen model.
    • Regulatory Compliance: Ensure the chosen model adheres to legal and industry-specific regulations.
  6. User Experience (UX) Design:
    • Interaction Design: Craft interaction strategies that are intuitive and aligned with user expectations.
    • User Interface: Design interfaces that facilitate effective communication and control between the user and AI.
  7. Safety and Security Analysis:
    • Safety Protocols: Ensure that the AI system operates safely, especially if interacting in physical or critical domains.
    • Security Measures: Assess and ensure the cybersecurity of the AI system.
  8. Scalability and Flexibility:
    • Adaptability: Ensure the chosen model can adapt to evolving technologies and user needs.
    • Scaling: Check that the model can scale to accommodate growing data, users, or task complexities.
  9. Prototype and Testing:
    • Develop Prototype: Create a prototype or Minimum Viable Product (MVP) based on the chosen model.
    • User Testing: Conduct user testing to gather feedback and observe how the AI system performs in real-world conditions.
  10. Continuous Evaluation:
  1. Implementation and Change Management:
  1. Post-Deployment Monitoring:
  1. Social and Cultural Considerations:
  1. Accessibility:
  1. Environmental Impact:
  1. Return on Investment (ROI) Analysis:
  1. Community and Stakeholder Involvement:
  1. Documentation and Transparency:
  1. Contingency Planning:
  1. Global and Local Perspectives:
  1. Emotional and Psychological Considerations:
  1. Future-Proofing:
  1. Public and Private Sector Implications:

Ethical considerations

When discussing AI relationships, it’s crucial to recognize that AI, regardless of its capabilities, does not possess consciousness, emotions, or subjective experiences. Thus, referring to them in contexts like “servant” or “slave” is purely metaphorical and should not anthropomorphize the technology.

Moreover, maintaining ethical use, user empathy, and respect for user rights and data are pivotal across all models. Ensuring that the development and deployment of AI adhere to robust ethical frameworks and respect human dignity and rights is paramount, regardless of the nature of the human-AI relationship.

As we progress towards more advanced AI systems, continuous dialogue and scrutiny regarding their roles, rights (if any), and ethical considerations should remain at the forefront of technological and societal discussions.

Other questions

  1. How does technology advances change which relationship model is best?
  2. How does the integration of AI and robotics change the relationship model?

How does technology advances change which relationship model is best?

Advancements in technology, especially in artificial intelligence, often reshape the potential and efficacy of different AI-human relationship models in various contexts. Let’s explore how technological progression could influence which relationship model is deemed best:

Enhanced Learning & Adaptability:

As AI becomes more adaptable and capable of learning in nuanced and context-rich environments, models like Mentor or Partner may become more viable and effective, offering more human-like interaction and support.

Improvements in Autonomous Decision-Making:

Technological strides that enable AI to make more informed and context-aware decisions autonomously might amplify the viability of the Autonomous Agent model, reducing the need for human oversight in certain tasks.

Emotion and Social Understanding:

Progress in emotion recognition and understanding of social dynamics could make AI more adept in roles like Mediator or Advisor, as it could factor in emotional and social considerations while facilitating or advising.

Enhanced Creativity:

Developments in generative algorithms and creative AI could bolster the Creator model, allowing AI to generate more innovative, contextually relevant, and emotionally resonant creations.

Advanced Analytics:

With AI achieving more profound analytical capabilities, the Analyzer model may offer deeper insights and predictions, fostering more informed decision-making in various domains.

Robust Security Mechanisms:

As AI becomes more proficient in identifying and mitigating cybersecurity threats, the Protector model might become paramount in safeguarding digital ecosystems.

Human-AI Collaboration:

Advancements facilitating seamless human-AI interaction and collaboration could elevate the Partner model, where both entities can effectively collaborate, comprehend, and augment each other’s capabilities.

High-Level Task Execution:

AI evolving to manage and execute more complex, non-linear tasks efficiently can strengthen the Executor model, handling more sophisticated tasks beyond simple, repetitive actions.

Ethical and Moral Decision Making:

If AI develops to comprehend and navigate ethical and moral dilemmas, models like Advisor or Mediator might become adept in navigating negotiations and decision-making processes involving ethical considerations.

Inclusive and Accessible Technology:

Making AI technology more accessible and user-friendly can enable wider adoption of various relationship models, catering to diverse needs across different user demographics and industries. While these examples illustrate potential shifts in model efficacy with technological advancement, it’s vital to recognize that the “best” relationship model may still be context-dependent, shaped by specific industry requirements, task natures, and ethical considerations. Future technological advancements might also introduce entirely new models of AI-human relationships that we have yet to conceive.

How does the integration of AI and robotics change the relationship model

The integration of AI and robotics broadens the scope and impact of AI-human relationship models by bringing them into the physical world. This amalgamation affects various aspects of the working relationship between humans and technology. Here’s how it potentially changes the relationship model:

Physical Interaction and Coexistence:

Combining robotics with AI allows for physical interaction, requiring relationship models that emphasize coexistence, safety, and collaboration in shared spaces, thus making Co-Pilot and Partner models especially pertinent.

Enhanced Autonomy:

The Autonomous Agent model becomes notably relevant as AI-powered robots can independently perform physical tasks, navigate environments, and make real-time decisions, reducing human intervention.

Real-World Problem-Solving:

The integration allows AI to interact with and manipulate the physical world, necessitating relationship models like the Executor and Protector to tackle tangible, real-world challenges and operations.

Human Skill Augmentation:

The Assistant model may evolve to encompass not just digital but also physical task assistance, as robotics can enhance human capabilities by physically aiding in tasks, e.g., lifting heavy objects or performing precision operations.

Physical Safety and Security:

Emphasizing the Protector model, robotics integrated with AI can monitor and safeguard physical spaces, performing security patrols, and potentially intervening in unsafe situations.

On-Site Decision Making:

With AI and robotics, decision-making processes such as the Advisor and Consultant models can be embedded in physical contexts, allowing robots to provide on-site advice and solutions based on real-time data and interactions.

Tactile Learning and Interaction:

As a Mentor, AI integrated with robotics can physically demonstrate tasks, interact tactilely, and guide users through hands-on learning experiences in various training contexts.

Physical Creation and Manufacturing:

The Creator model becomes crucial in manufacturing and construction, where robots, driven by AI, can design, create, and assemble physical objects, innovating product development and production processes.

Social and Emotional Interaction:

AI-driven robots can engage in social interactions, potentially acting as companions or social mediators, although ensuring genuine and ethically sound emotional engagement remains a challenge. Accessibility and Personal Assistance:

For individuals with disabilities or mobility issues, robotics, and AI can function as personal assistants, facilitating everyday tasks, mobility, and enhancing independence and quality of life. Dynamic Adaptation:

AI-robotics amalgamation can physically adapt to dynamic environments and user needs, embodying various models (like Observer, Monitor, or Facilitator) to seamlessly integrate into diverse workflows and contexts.

In these ways, AI and robotics together bring a multidimensional, tangible aspect to AI-human interactions. Different relationship models may become more or less applicable depending on the physical, social, and environmental contexts, and the ethical considerations become particularly paramount due to the direct impact on the physical world and human lives. This integration requires meticulously crafted frameworks to ensure safety, ethics, and utility in co-creating value in shared human-machine environments.