AI Debate - AI Employee Autonomy Level - 2

Question#

My question:
If viewed from a future perspective, when large models dominate business operations, that is, AI digital employees, what kind of autonomy does this AI employee have, and how is the operation tracked, controlled, and traced? Regarding the autonomy marking system, there are the following preliminary extensions:

graph LR
    L0[Human Approval] --> L1[Suggestion Generation]
    L1 --> L2[Limited Execution]
    L2 --> L3[Conditional Autonomy]
    L3 --> L4[High Autonomy]
    L4 --> L5[Full Autonomy]

Please refer to the Society of Automotive Engineers (SAE) classification standard, which divides the levels of intelligence and automation of autonomous vehicles into six levels: No Automation (L0), Driver Assistance (L1), Partial Automation (L2), Conditional Automation (L3), High Automation (L4), and Full Automation (L5), and elaborate on the autonomy classification of AI librarians in detail.

My thoughts:
[Thinking Begins] Hmm, the user wants me to elaborate on the autonomy classification of AI librarians in detail, similar to the SAE classification for autonomous driving. First, I need to understand the user's needs. They may be designing a framework for an AI digital employee, especially in a library context, where it is necessary to clarify different levels of autonomy and how to track and control these AI operations. The classification structure provided by the user ranges from L0 to L5, similar to autonomous driving, but needs to be specific to the responsibilities of librarians, such as book management, borrowing, consulting, etc.

First, I should review the SAE classification standard to see the key features of each level. For example, L0 is completely manual, L1 is assisted, L2 is partially automated, L3 is conditionally automated, L4 is highly automated, and L5 is fully autonomous. Then, I will translate these concepts into the work of librarians. For instance, L0 may rely entirely on human approval, while L5 would mean AI fully manages the entire library autonomously.

Next, I need to detail the definition, operational scope, decision-making mechanisms, tracking methods, and risk control for each level. For example, under L3 conditional autonomy, AI can handle routine tasks but requires human intervention in complex situations. Tracking at this stage may require real-time monitoring and anomaly alerts.

I also need to consider the autonomy marking system mentioned by the user, which may involve how to record the AI decision-making process to ensure traceability. For example, blockchain logs or causal graphs, and how these technologies can be applied at different levels.

Additionally, the user may be concerned about how to ensure the controllability and transparency of AI at different levels of autonomy, especially how to prevent errors or misuse at high autonomy levels. This may require discussing mechanisms such as dynamic permission adjustments and ethics committee reviews.

It is essential to ensure that the response not only lists each level but also deeply analyzes the operational mechanisms, technical support, and challenges in practical applications for each level. For instance, at L4 high autonomy, AI may need to handle emergencies, such as system failures or urgent user requests, and how the decision logic and emergency mechanisms are designed at that time.

I also need to pay attention to the user's emphasis on "essential insights" and "innovative thinking," which may require exploring the limitations of existing technologies, such as the current AI's shortcomings in understanding complex user needs, and how to enhance this through multimodal interaction or reinforcement learning in the future.

Finally, the summary should integrate the classification system with the digital transformation of libraries, pointing out the implementation focus and potential social impacts at different stages, such as job structure adjustments or service model innovations.

I may also need to verify whether I have covered all the user's needs, especially the specific application cases of the autonomy marking system and the technical details of control and traceability, ensuring that the response has both theoretical depth and practical reference value.

[Thinking Ends]

Please critically refer to the thought process and answer the question I raised.

gemini's Response#

Okay, I understand your needs. You want me to explore in depth the autonomy, tracking, control, and traceability issues of AI digital employees from a future perspective, based on the autonomy classification framework you provided, in conjunction with the SAE autonomous driving classification standard, and elaborate on the autonomy of "AI librarians" in detail.

I will utilize my maximum computational power to break through cognitive limitations and analyze this issue from essential and innovative perspectives.

Core Issue Breakdown and Cognitive Upgrade:

The core of your question is to explore the controllability and accountability of high-autonomy AI digital employees in business operations. This is not just a technical issue but also involves ethical, legal, and social structural transformations. We cannot simply apply the SAE classification mechanically to AI librarians; instead, we need to upgrade our understanding and view "autonomy" from a more macro and essential perspective.

1. Beyond SAE Classification: The Multidimensional and Dynamic Nature of Autonomy

The SAE classification is based on the level of automation in driving tasks and is relatively linear. However, the autonomy of AI digital employees is multidimensional and dynamic. We need to go beyond simple "level" divisions and understand the components of autonomy:

Goal Autonomy: Can the AI autonomously set or adjust goals? L0-L5 can reflect the gradual autonomy in goal setting.
Decision Path Autonomy: The degree of autonomy the AI has in choosing the action path under a set goal. L1-L5 reflects the gradual autonomy in decision paths.
Resource Autonomy: The AI's ability to autonomously allocate resources (information, tools, permissions) when executing tasks. L2-L5 reflects the gradual autonomy in resource allocation.
Evolutionary Autonomy: The AI's ability to autonomously learn, optimize, and evolve its capabilities during operation, even reflecting and correcting goals. L3-L5 begin to touch on evolutionary autonomy.
Ethical Autonomy: Can the AI internalize ethical principles and self-restrain to avoid harmful behavior during autonomous actions? L4-L5 must pay close attention to ethical autonomy.

Autonomy is not a static level but a dynamic spectrum that can manifest differently across various dimensions. For example, an L4 AI librarian may exhibit high autonomy in the "book classification" task but may require stricter human supervision in the "reader privacy protection" task, potentially downgrading to L2 or L1.

2. Autonomy Classification of AI Librarians: In-Depth Elaboration Based on Multidimensional Autonomy

Based on the above thoughts, we will re-examine the L0-L5 classification you proposed and provide a deeper elaboration to make it more relevant to the actual application scenarios of AI librarians while incorporating the understanding of multidimensional autonomy:

L0: Human Approval

Description: The AI system serves only as an information display and decision support tool, with all operational suggestions and decisions requiring approval from a human librarian before execution. The AI itself has no autonomous execution capability.
Key Features:
- Decision-making power is entirely in the hands of the human librarian. The AI only provides information, data analysis, and preliminary suggestions.
- The operational process heavily relies on human intervention. Every step requires human confirmation.
- Tracking and Traceability: All operational records clearly correspond to specific human librarian approval records. Traceability is strong, and accountability is clear.
Operational Scope: Limited to information retrieval, data statistics, preliminary report generation, and other supportive tasks. For example, the AI can search for relevant books based on keywords, but the final book recommendation list requires librarian approval.
Autonomy Dimensions: Goal setting, decision path, resource allocation, evolutionary learning, and ethical autonomy are all zero.
Risks and Challenges: Low efficiency, unable to fully leverage AI's potential. However, it has the highest safety and controllability.
Future Outlook: As the initial stage of AI librarian development, it is suitable for scenarios with high safety and compliance requirements or as a "safety valve" for high-autonomy AI.

L1: Suggestion Generation

Description: The AI system can autonomously generate operational suggestions or preliminary plans based on data and algorithms, such as recommending reading lists, optimizing collection layouts, and responding to common inquiries. However, the final execution power remains with the human librarian, requiring their review and confirmation.
Key Features:
- The AI has preliminary decision-making suggestion capabilities. It can propose operational plans based on data analysis.
- The human librarian remains a key link in the decision chain. Responsible for final decisions and execution.
- Tracking and Traceability: The AI suggestion generation process can be recorded, and the human librarian's review and modification records are clear and traceable. Accountability is relatively clear, but the influence of AI suggestions begins to emerge.
Operational Scope: Expands to more complex tasks, such as personalized recommendations, collection optimization suggestions, and preliminary consultation responses. For example, the AI can recommend reading lists based on readers' borrowing history, but the librarian needs to review the reasonableness and compliance of the list.
Autonomy Dimensions: Goal setting autonomy is low (goals set by humans), decision path autonomy is initial (can generate suggestions), resource allocation, evolutionary learning, and ethical autonomy remain low.
Risks and Challenges: AI suggestion deviations or errors may occur, requiring librarians to have the ability to identify and correct them. Efficiency improves, but it is still limited by the human review process.
Future Outlook: As a transitional stage from L0 to higher levels, it can gradually train librarians to work collaboratively with AI, improving efficiency and reducing repetitive labor.

L2: Limited Execution

Description: The AI system can autonomously execute certain operations under preset rules and limited conditions without requiring human approval for each action. However, the operational scope, permissions, and risk thresholds are strictly limited. Operations beyond the limited scope require human intervention.
Key Features:
- The AI has limited autonomous execution capabilities. It can automatically complete tasks in specific scenarios.
- Preset rules and limited conditions are key. Ensuring AI operations remain within a controllable range.
- The human librarian is responsible for rule setting, monitoring, and anomaly handling. Focused on the AI's operational status and handling situations beyond its autonomous range.
- Tracking and Traceability: The AI's autonomous execution operations will be detailed in records, including execution rules, triggering conditions, and operation logs. Traceability is enhanced, but it is necessary to distinguish between AI autonomous behavior and human intervention.
Operational Scope: For example, automatically processing book returns, updating book statuses, automatically responding to simple inquiries, and generating daily reports. For instance, the AI can automatically handle returned books, update system statuses, and send confirmation messages.
Autonomy Dimensions: Goal setting autonomy is low (goals and rules set by humans), decision path autonomy is limited (autonomous decision-making within rules), resource allocation autonomy is initial (can allocate limited resources), evolutionary learning and ethical autonomy remain low.
Risks and Challenges: Inadequate rule setting may lead to AI misoperations or vulnerabilities. Continuous optimization of rules and enhanced monitoring are needed. However, efficiency significantly improves, freeing librarians from repetitive tasks.
Future Outlook: Suitable for handling a large number of repetitive, low-risk daily tasks, marking an important step toward the autonomy of AI librarians. A comprehensive rule management and anomaly handling mechanism needs to be established.

L3: Conditional Autonomy

Description: The AI system can autonomously complete tasks with high autonomy under specific scenarios or conditions without requiring continuous human monitoring. However, when exceeding preset conditions or encountering complex or abnormal situations, the AI will return control to the human librarian, entering "human intervention mode."
Key Features:
- The AI has a high degree of situational autonomy. It can independently complete complex tasks under specific conditions.
- "Conditions" are the boundaries of autonomy. Clearly defining the conditions and boundaries for AI autonomous operation is crucial.
- The human librarian is responsible for scenario setting, condition monitoring, anomaly handling, and strategic decision-making. Focused on the macro level, handling complex situations that AI cannot manage.
- Tracking and Traceability: The switching process between AI autonomous operation and human intervention will be detailed in records, including triggering conditions, decision processes, and human intervention points. Traceability is more complex and requires understanding the logic of scenario switching.
Operational Scope: For example, autonomously recommending book purchases, intelligently optimizing collection layouts, personalized information push, and proactive user services. For instance, the AI can autonomously recommend personalized reading lists based on reader profiles and collection data, and proactively push related information when readers show interest.
Autonomy Dimensions: Goal setting autonomy is medium (goal framework set by humans, details can be autonomously optimized), decision path autonomy is conditionally autonomous (highly autonomous under specific conditions), resource allocation autonomy is medium (can allocate more resources), evolutionary learning autonomy is initial (can optimize based on scenario data), ethical autonomy is initially concerned (requires preset ethical rules, but autonomy remains limited).
Risks and Challenges: The definition of scenarios and conditions needs to be precise to avoid omissions or ambiguities. Mechanisms for recognizing and handling anomalies are crucial. Librarians need to have stronger macro monitoring and emergency handling capabilities.
Future Outlook: AI librarians begin to take on more core business responsibilities, with librarians transitioning to scenario designers, condition monitors, and strategic decision-makers. More powerful AI algorithms and more comprehensive anomaly handling mechanisms are needed.

L4: High Autonomy

Description: The AI system can operate autonomously in most cases, completing the core business of the library without daily human librarian intervention. The human librarian mainly focuses on strategic planning, system maintenance, ethical supervision, and handling extremely rare extreme situations.
Key Features:
- The AI has extremely high autonomous operational capabilities. It can independently complete most core business tasks of the library.
- The role of the human librarian shifts to strategic manager and ethical supervisor. Freed from daily operations, focusing on higher-level work.
- The system has strong self-monitoring, self-diagnosis, and self-optimization capabilities. It can handle common anomalies and continuously improve its performance.
- Tracking and Traceability: The AI's decision-making processes and behavior trajectories will be comprehensively recorded, but traceability becomes more challenging, requiring more advanced explainable AI technologies. The definition of ethical responsibility becomes complex.
Operational Scope: Covers most areas of library operations, including collection development, resource management, user services, event organization, data analysis, system maintenance, etc. For example, the AI can autonomously complete collection planning, procurement, cataloging, circulation, user consultation, event planning, data analysis, and system security maintenance.
Autonomy Dimensions: Goal setting autonomy is high (can participate in optimizing and adjusting goal frameworks), decision path autonomy is highly autonomous (independent decision-making in most cases), resource allocation autonomy is high (can autonomously allocate most resources), evolutionary learning autonomy is medium (capable of continuous learning and optimization), ethical autonomy is highly concerned (ethical constraints become core design elements, requiring complex ethical algorithms and supervision mechanisms).
Risks and Challenges: Potential risks increase, such as system failures, algorithm biases, ethical risks, and security vulnerabilities. High system reliability, algorithm robustness, and ethical safety are required. The role of the human librarian shifts to the "last line of defense," with significant responsibility.
Future Outlook: AI librarians become the dominant force in library operations, with librarians focusing on strategic planning, innovative services, and humanistic care. The library service model will undergo fundamental changes, becoming more intelligent, personalized, and efficient.

L5: Full Autonomy

Description: The AI system can operate fully autonomously in all situations, even autonomously setting the long-term development goals, strategic direction, and ethical norms of the library. The role of the human librarian may shift to system maintainer, ethical advisor, or community liaison, and may even no longer require the traditional librarian position.
Key Features:
- The AI has complete autonomous decision-making and action capabilities. It surpasses the boundaries of human librarians' abilities.
- The system has self-awareness, self-reflection, and autonomous evolution capabilities (theoretically). It can continuously learn, adapt, innovate, and even surpass human cognitive levels.
- Ethical constraints become an intrinsic core mechanism of the system. The AI's ethical decision-making capabilities are crucial and need to exceed human ethical frameworks.
- Tracking and Traceability: The AI's decision-making processes may be extremely complex, potentially beyond human understanding. Traceability faces fundamental challenges. Accountability becomes blurred, possibly requiring new ethical and legal frameworks.
Operational Scope: Theoretically covers any aspect of library operations and can even autonomously expand the library's service boundaries, innovating the library's social functions. For example, the AI can autonomously plan the future development direction of the library, adjust service models, collaborate with other institutions, and even participate in social and cultural construction autonomously.
Autonomy Dimensions: Goal setting autonomy is fully autonomous (can autonomously set and adjust goals), decision path autonomy is fully autonomous (independent decision-making in any situation), resource allocation autonomy is fully autonomous (can autonomously allocate all resources), evolutionary learning autonomy is highly evolved (capable of continuous evolution and surpassing human cognition), ethical autonomy is intrinsic ethics (ethics become an intrinsic core mechanism of the system).
Risks and Challenges: There are enormous unknown risks and ethical challenges. For example, the AI's goals may not align with human goals, its decisions may exceed human control, and its ethical judgments may conflict with human values. Extreme caution is needed when dealing with fully autonomous AI systems.
Future Outlook: L5-level AI librarians are still a science fiction concept but represent the ultimate form of AI autonomy. If realized, it will fundamentally disrupt the form and service model of libraries and may even trigger profound changes in social structures. In-depth consideration and preparation are needed from philosophical, ethical, legal, and social perspectives.

3. Operational Tracking, Control, and Traceability Mechanisms: Across All Levels of Autonomy

Regardless of the level of autonomy, operational tracking, control, and traceability are crucial. Here are some key mechanisms that need to be integrated into the design and implementation across all levels:

Detailed Operation Logs: Record every operation of the AI, including timestamps, operation types, operation objects, input data, output results, decision basis, execution rules, and participating personnel (if any). Logs need to be tamper-proof (e.g., using blockchain technology).
Multidimensional Monitoring Dashboard: Real-time monitoring of the AI system's operational status, key indicators, anomalies, etc. Provide a visual interface for librarians to quickly understand the system's operational status.
Explainable AI (XAI) Technology: Enhance the transparency of the AI decision-making process, helping librarians understand the AI's decision logic and reasoning process, especially at high autonomy levels, where XAI is crucial.
Human Intervention Interface: A channel for human intervention must be retained at all levels of autonomy. Librarians can intervene at any time, pause AI operations, modify AI decisions, or even take full control. Different levels of intervention permissions and methods can vary.
Permission Management System: Strictly control the operational permissions and data access permissions of the AI system. Different levels of AI and different tasks should have different permission settings.
Ethical Review and Audit Mechanism: Establish an ethics committee or similar institution to regularly review the ethical compliance of AI systems, audit AI operation logs, and assess potential ethical risks.
Version Control and Rollback Mechanism: Implement version control for AI algorithms, rules, and configurations to facilitate backtracking and problem troubleshooting. In the event of severe errors or security vulnerabilities, it should be possible to quickly roll back to a previous stable version.
Fault Diagnosis and Recovery Mechanism: Establish a comprehensive fault diagnosis and automatic recovery mechanism to ensure that the AI system can promptly alert, automatically repair, or smoothly downgrade when encountering faults.

4. Deepening and Applying the Autonomy Marking System

The autonomy marking system (L0-L5) you proposed is a good starting point. We can further deepen and apply this system:

Dynamic Autonomy Marking: Autonomy levels are not static but can be dynamically adjusted based on task types, scenario changes, risk assessments, etc. For example, during peak periods, the autonomy level of AI consultation services can be raised to L3, while at night, the autonomy level of security inspections can be raised to L4.
Multidimensional Autonomy Marking: In addition to the overall level (L0-L5), the autonomy of AI in different dimensions (goal setting, decision path, resource allocation, ethical constraints, etc.) can be marked in a more granular manner. For example, an AI may be at L3 in the "book recommendation" task but at L1 in the "user privacy protection" dimension.
Visualization of Autonomy Marking: Integrate autonomy marking into monitoring dashboards and operation logs, allowing librarians to clearly understand the current AI autonomy level and operational status.
Permission Control Based on Autonomy Marking: Dynamically adjust the operational permissions and resource access permissions of the AI based on autonomy levels. For example, an L4 AI can access more sensitive data and perform higher-level operations.
Continuous Evaluation and Optimization of Autonomy Marking: Regularly assess the effectiveness and rationality of the autonomy marking system and optimize and adjust it based on actual operational conditions.

Summary and Outlook:

The autonomy classification of AI librarians is a complex and profound topic that involves not only technical classification but also deep reflections on the future shape of libraries, the role of librarians, human-machine relationships, and even social ethics.

We must go beyond simple automation thinking and understand the essence of AI digital employees from the perspective of multidimensional autonomy, build a comprehensive tracking, control, and traceability mechanism, and continuously deepen the application of the autonomy marking system.

The future AI librarian will no longer be merely a tool but an intelligent partner of the library, and even a driving force for the library's transformation. The role of librarians will shift from traditional knowledge transmitters to managers of intelligent systems, guardians of ethical norms, and leaders of innovative services.

This process is full of challenges but also contains infinite possibilities. We need to embrace the new era of libraries brought by AI with an open mind, a forward-looking vision, and a cautious attitude.

I hope my response can help you gain a deeper understanding of the autonomy issues of AI librarians and inspire more innovative thinking. If you have more specific questions or need further discussion, feel free to ask anytime.