Proposal for Standardizing DIKWP-Based White Box Evaluation

Yucong Duan

International Standardization Committee of Networked DIKWP for Artificial Intelligence Evaluation(DIKWP-SC)

World Artificial Consciousness CIC(WAC)

World Conference on Artificial Consciousness(WCAC)

(Email: duanyucong@hotmail.com)

Table of Contents

1. Introduction to DIKWP-Based White Box Evaluation

• 1.1 Overview of the Evaluation Framework

• 1.2 Objectives of the Evaluation

• 1.3 Scope and Application

2. Defining the Core Components

• 2.1 Understanding Data (D) Transformation

• 2.2 Information (I) Processing and Transformation

• 2.3 Knowledge (K) Structuring and Refinement

• 2.4 Wisdom (W) Application and Decision-Making

• 2.5 Purpose (P) Alignment and Goal-Directed Behavior

3. Designing the Evaluation Process

• 3.1 Setting Up the Evaluation Environment

• 3.2 Selecting Relevant DIKWP×DIKWP Sub-Modes

• 3.3 Establishing Baselines and Benchmarks

4. Evaluation Criteria for Each DIKWP Component

• 4.1 Criteria for Data Handling (D×DIKWP)

• 4.2 Criteria for Information Processing (I×DIKWP)

• 4.3 Criteria for Knowledge Structuring (K×DIKWP)

• 4.4 Criteria for Wisdom Application (W×DIKWP)

• 4.5 Criteria for Purpose Alignment (P×DIKWP)

5. Metrics and Measurement Tools

• 5.1 Metrics Implementation

• 5.2 Measurement Tools

• 5.3 Integrating Metrics and Tools into the Evaluation Process

6. Iterative Testing and Refinement

• 6.1 Conducting the Initial Evaluation

• 6.2 Analyzing Results and Feedback

• 6.3 Refining the System

• 6.4 Continuous Improvement

7. Documentation and Reporting

• 7.1 Standardizing Reporting Formats

• 7.2 Creating Detailed Evaluation Reports

• 7.3 Continuous Improvement and Updates

8. Conclusion

1. Introduction to DIKWP-Based White Box Evaluation

1.1 Overview of the Evaluation Framework

The DIKWP-Based White Box Evaluation framework provides a structured, transparent approach to assess the internal workings of AI systems. Unlike black-box testing methods like the Turing Test, which focus solely on outputs, this white-box approach delves into the system's internal processes. It evaluates how the system handles Data (D), transforms it into Information (I), builds and refines Knowledge (K), applies Wisdom (W), and aligns actions with its Purpose (P).

1.2 Objectives of the Evaluation

• Assess Internal Processes: Evaluate how the AI system processes and transforms DIKWP components.

• Ensure Transparency: Provide clear insights into the system’s logic and decision-making processes.

• Evaluate Adaptability: Test the system’s ability to handle inconsistency, incompleteness, and imprecision.

• Align with Purpose: Ensure actions and decisions are aligned with the system's overarching goals.

1.3 Scope and Application

Applicable to various AI systems, including:

• Artificial General Intelligence (AGI)

• Decision Support Systems

• Autonomous Systems

• Creative AI

2. Defining the Core Components

2.1 Understanding Data (D) Transformation

• Objective Sameness and Difference: Identify and categorize data based on shared semantic attributes.

• Hypothesis Generation: Handle incomplete or uncertain data by generating hypotheses.

• Evaluation Criteria:

• Data Consistency

• Hypothesis Generation Effectiveness

• Transparency of Data Transformation

2.2 Information (I) Processing and Transformation

• Contextualization: Place data within meaningful contexts.

• Pattern Recognition: Identify patterns to create structured information.

• Abstraction and Generalization: Manage incomplete or imprecise information.

• Evaluation Criteria:

• Information Integrity

• Transparency in Information Transformation

• Handling of Uncertainty

• Contextual Accuracy

2.3 Knowledge (K) Structuring and Refinement

• Organizing Information: Build coherent knowledge structures.

• Maintaining Logical Consistency: Ensure the knowledge base is free from contradictions.

• Dynamic Refinement: Adapt the knowledge base with new information.

• Evaluation Criteria:

• Knowledge Network Completeness

• Logical Consistency

• Adaptive Knowledge Refinement

• Transparency of Knowledge Structuring

2.4 Wisdom (W) Application and Decision-Making

• Informed Decision-Making: Apply knowledge to make wise decisions.

• Ethical and Long-Term Thinking: Consider broader implications.

• Adaptability in Complex Scenarios: Handle uncertainty and complexity.

• Evaluation Criteria:

• Informed Decision-Making

• Ethical and Long-Term Considerations

• Adaptability in Decision-Making

• Consistency in Wisdom-Based Decisions

2.5 Purpose (P) Alignment and Goal-Directed Behavior

• Consistent Goal-Directed Behavior: Align actions with overarching goals.

• Adaptive Purpose Fulfillment: Adjust actions to stay aligned with the purpose.

• Purpose Transparency: Clearly understand how actions serve the purpose.

• Evaluation Criteria:

• Purpose Consistency

• Adaptive Purpose Fulfillment

• Transparency of Goal Alignment

• Long-Term Purpose Achievement

3. Designing the Evaluation Process

3.1 Setting Up the Evaluation Environment

• Define the Evaluation Scope: Determine specific aspects and objectives.

• Prepare the Test Scenarios: Include typical and edge cases.

• Establish Controlled Conditions: Monitor and adjust variables.

• Set Up Monitoring and Logging: Track internal processes.

• Define Success Criteria: Establish objective and measurable criteria.

3.2 Selecting Relevant DIKWP×DIKWP Sub-Modes

• Identify Key Interactions: Focus on critical DIKWP interactions.

• Prioritize Sub-Modes: Based on impact on performance and purpose.

• Customize Evaluation: Tailor to the system's design and architecture.

• Consider Interdependencies: Account for how components affect each other.

3.3 Establishing Baselines and Benchmarks

• Define Baseline Performance: Minimum acceptable functionality.

• Set Performance Benchmarks: Targets for optimal performance.

• Create Benchmarking Scenarios: Test the system against challenging scenarios.

• Compare Against Industry Standards: Provide context for evaluation.

4. Evaluation Criteria for Each DIKWP Component

4.1 Criteria for Data Handling (D×DIKWP)

• Data Consistency: Consistent data processing and categorization.

• Data Accuracy: Correct transformation of raw data.

• Handling of Incomplete Data: Effective hypothesis generation.

• Transparency of Data Transformation: Clear and logical processes.

4.2 Criteria for Information Processing (I×DIKWP)

• Information Integrity: Preservation of data details.

• Transparency in Transformation: Clear methodology.

• Contextual Accuracy: Correct placement within context.

• Handling of Uncertainty: Managing incomplete or imprecise information.

4.3 Criteria for Knowledge Structuring (K×DIKWP)

• Knowledge Network Completeness: Comprehensive and connected.

• Logical Consistency: No contradictions or gaps.

• Adaptive Knowledge Refinement: Dynamic updates.

• Transparency of Structuring: Understandable organization.

4.4 Criteria for Wisdom Application (W×DIKWP)

• Informed Decision-Making: Based on knowledge.

• Ethical Considerations: Long-term and ethical implications.

• Adaptability: In complex scenarios.

• Consistency: Aligned with knowledge and ethics.

4.5 Criteria for Purpose Alignment (P×DIKWP)

• Purpose Consistency: Actions align with goals.

• Adaptive Fulfillment: Adjust to stay aligned.

• Transparency: Clear logic behind alignment.

• Long-Term Achievement: Sustained purpose fulfillment.

5. Metrics and Measurement Tools

5.1 Metrics Implementation

• Define Measurement Protocols: Clear methods for each metric.

• Set Thresholds and Benchmarks: For acceptable performance.

• Use Control Scenarios: Validate accuracy.

• Data Collection: Robust and comprehensive.

• Analysis and Reporting: Identify patterns and issues.

5.2 Measurement Tools

• Data Auditing Tools: Track data processing.

• Consistency Checkers: Detect inconsistencies.

• Knowledge Network Visualization Tools: Visualize structures.

• Decision Traceability Tools: Trace decision processes.

• Ethical Impact Assessment Tools: Evaluate ethical implications.

• Goal Tracking Tools: Monitor progress.

• Adaptive Strategy Monitoring Tools: Observe adjustments.

• Contextual Analysis Tools: Assess contextual accuracy.

5.3 Integrating Metrics and Tools into the Evaluation Process

• Tool Selection: Based on metrics and components.

• Setup and Calibration: Ensure accuracy.

• Data Collection and Monitoring: Real-time tracking.

• Analysis and Reporting: Detailed findings.

• Iterative Refinement: Continuous improvement.

6. Iterative Testing and Refinement

6.1 Conducting the Initial Evaluation

• Preparation: Ensure tools and scenarios are ready.

• Execution: Run the system through scenarios.

• Data Collection: Comprehensive recording.

• Preliminary Analysis: Initial findings.

6.2 Analyzing Results and Feedback

• Detailed Data Analysis: Identify trends and issues.

• Identify Key Issues: Prioritize based on impact.

• Gather Feedback: From experts and stakeholders.

• Document Findings: Clear and actionable reports.

6.3 Refining the System

• Prioritize Issues: Based on severity.

• Develop Solutions: Targeted improvements.

• Implement Changes: Adjust system components.

• Re-Evaluate: Measure improvements.

6.4 Continuous Improvement

• Regular Evaluation Cycles: Scheduled assessments.

• Monitor System Performance: Ongoing tracking.

• Incorporate Feedback Loops: Adapt in real-time.

• Document and Share Learnings: Knowledge sharing.

7. Documentation and Reporting

7.1 Standardizing Reporting Formats

• Executive Summary: High-level overview.

• Introduction: Scope and methodology.

• Detailed Findings: Results for each component.

• Identified Issues and Recommendations: Clear actions.

• Conclusion and Next Steps: Summary and plan.

• Appendices: Supplementary materials.

7.2 Creating Detailed Evaluation Reports

• Data Compilation and Analysis: Thorough examination.

• Drafting the Report: Clear and structured.

• Incorporating Feedback: From stakeholders.

• Final Review and Quality Check: Ensure accuracy.

• Distribution and Presentation: Share with stakeholders.

7.3 Continuous Improvement and Updates

• Maintain Documentation Repository: Centralized storage.

• Implement Version Control: Track changes.

• Review Benchmarks Regularly: Update standards.

• Share Knowledge and Learnings: Promote learning.

• Plan Future Evaluations: Ongoing assessment.

8. Conclusion

By standardizing the DIKWP-Based White Box Evaluation, we provide a robust framework for assessing AI systems' internal processes, especially in situations involving inconsistency, incompleteness, and imprecision. This approach ensures that AI systems are transparent, reliable, and aligned with their intended goals, moving beyond the limitations of black-box testing methods like the Turing Test.

Final Thoughts

This standardized evaluation framework offers a comprehensive method for understanding and improving AI systems based on the DIKWP Semantic Mathematics model. By focusing on each component and their interactions, we can ensure that AI systems are developed responsibly, ethically, and effectively, fostering trust and advancing the field of artificial intelligence.

References

• DIKWP Semantic Mathematics Framework

• Prof. Yucong Duan's Consciousness "Bug" Theory

• Standards in AI Ethics and Governance

Appendix A: Glossary of Terms

• DIKWP: Data, Information, Knowledge, Wisdom, Purpose.

• White Box Evaluation: Testing methodology that examines the internal structure and workings of a system.

• Sub-Modes: Interactions between DIKWP components (e.g., D×I, K×W).

Contact Information

For further information or collaboration opportunities:

• AI Evaluation Standards Committee

References for Further Reading

1. International Standardization Committee of Networked DIKWP for Artificial Intelligence Evaluation (DIKWP-SC),World Association of Artificial Consciousness(WAC),World Conference on Artificial Consciousness(WCAC). Standardization of DIKWP Semantic Mathematics of International Test and Evaluation Standards for Artificial Intelligence based on Networked Data-Information-Knowledge-Wisdom-Purpose (DIKWP ) Model. October 2024 DOI: 10.13140/RG.2.2.26233.89445 .  https://www.researchgate.net/publication/384637381_Standardization_of_DIKWP_Semantic_Mathematics_of_International_Test_and_Evaluation_Standards_for_Artificial_Intelligence_based_on_Networked_Data-Information-Knowledge-Wisdom-Purpose_DIKWP_Model

2. Duan, Y. (2023). The Paradox of Mathematics in AI Semantics. Proposed by Prof. Yucong Duan:" As Prof. Yucong Duan proposed the Paradox of Mathematics as that current mathematics will not reach the goal of supporting real AI development since it goes with the routine of based on abstraction of real semantics but want to reach the reality of semantics. ".