Clarifying the Association of the 3-No Problems within the DIKWP*DIKWP Interaction Space

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)

Abstract

Within the Data-Information-Knowledge-Wisdom-Purpose (DIKWP) model, effective communication between stakeholders—whether human or artificial intelligence (AI) systems—is crucial for achieving mutual understanding and collaboration. Previously, the 3-No Problems—Incomplete Input/Output, Inconsistent Input/Output, and Imprecise Input/Output—were associated specifically with Data, Information, and Knowledge, respectively. However, this association may appear restrictive and may not fully encapsulate the versatile nature of these communication deficiencies. This document revisits the mapping of the 3-No Problems within the DIKWP*DIKWP interaction space, providing a nuanced understanding and comprehensive examples that demonstrate their applicability across various DIKWP components.

1. Introduction

The DIKWP model structures cognitive processes into five interconnected components: Data (D), Information (I), Knowledge (K), Wisdom (W), and Purpose (P). Communication between stakeholders involves the exchange of these components, but deficiencies can disrupt this process. The 3-No Problems—Incomplete Input/Output, Inconsistent Input/Output, and Imprecise Input/Output—are critical barriers to effective communication and understanding.

In previous discussions, these problems were associated specifically with Data, Information, and Knowledge. However, this association may inadvertently imply that each No Problem exclusively pertains to one DIKWP component. This document clarifies that while certain No Problems may predominantly affect specific components, they are fundamentally cross-cutting issues that can impact any of the DIKWP components depending on the context of the interaction.

2. Revisiting the Association of 3-No Problems with DIKWP Components2.1 Initial Association: A Simplistic Mapping

Previously, the 3-No Problems were mapped as follows:

1. Incomplete Input/Output (No-Incomplete): Associated with Data (D)

2. Inconsistent Input/Output (No-Inconsistent): Associated with Information (I)

3. Imprecise Input/Output (No-Imprecise): Associated with Knowledge (K)

This mapping was intended for illustrative purposes to provide clear, focused examples. However, it simplifies the multifaceted nature of communication deficiencies within the DIKWP framework.

2.2 Comprehensive Association: Cross-Cutting Nature of 3-No Problems

The 3-No Problems are not inherently limited to specific DIKWP components. Instead, they are general communication deficiencies that can manifest across any of the five DIKWP components—Data (D), Information (I), Knowledge (K), Wisdom (W), and Purpose (P)—depending on the nature of the interaction and the specific context.

Key Points:

• Incomplete Input/Output (No-Incomplete): Can affect Data, Information, Knowledge, Wisdom, and Purpose by lacking necessary elements required for comprehensive understanding or action.

• Inconsistent Input/Output (No-Inconsistent): Can introduce contradictions or discrepancies across any DIKWP components, leading to confusion and misalignment.

• Imprecise Input/Output (No-Imprecise): Can cause ambiguity or vagueness in any DIKWP component, undermining clarity and effective decision-making.

3. Detailed Examples Illustrating the Cross-Cutting Nature of the 3-No Problems3.1 Incomplete Input/Output (No-Incomplete)

Definition:Incomplete Input/Output occurs when one stakeholder lacks sufficient components (Data, Information, Knowledge, Wisdom, or Purpose) to fully comprehend or act upon the DIKWP elements being communicated by the other stakeholder.

Examples Across DIKWP Components:

1. Data (D): Human-AI Environmental Monitoring

   Scenario:

   An environmental scientist (Human) collaborates with an AI system (AI-Monitor) to monitor air quality. The AI provides Data vectors representing pollutant levels across various locations.

   Incomplete Data:

   Impact:

   The missing Data from critical sensors (ΔDA\Delta \mathbf{D}_AΔDA​) leads to incomplete monitoring, potentially overlooking hazardous pollution spikes.

   Remediation:

   The Human provides the missing Data to the AI, ensuring comprehensive monitoring across all critical locations.

• Human's Data (DH\mathbf{D}_HDH​): Pollutant levels at specific sensors.

• AI's Data (DA\mathbf{D}_ADA​): Pollutant levels but missing data from certain critical sensors.

2. Information (I): Human-AI Supply Chain Management

   Scenario:

   A supply chain manager (Human) works with an AI system (AI-Optimizer) to optimize inventory levels. The AI analyzes Information regarding current stock, demand forecasts, and supplier lead times.

   Incomplete Information:

   Impact:

   The lack of real-time stock updates leads to suboptimal inventory decisions, risking overstocking or stockouts.

   Remediation:

   The Human integrates real-time stock Data into the AI's Information set, enhancing the AI's ability to make accurate inventory optimizations.

• Human's Information (IH\mathbf{I}_HIH​): Demand forecasts and current stock levels.

• AI's Information (IA\mathbf{I}_AIA​): Supplier lead times but lacks real-time stock updates.

3. Knowledge (K): Human-AI Educational Platform

   Scenario:

   An educator (Human) uses an AI-powered educational platform (AI-Edu) to provide personalized learning paths for students.

   Incomplete Knowledge:

   Impact:

   The AI's limited Knowledge leads to generic recommendations that do not cater to individual student needs, reducing the effectiveness of personalized learning.

   Remediation:

   The Human enriches the AI's Knowledge base with detailed insights into student progress and learning styles, enabling more tailored and effective recommendations.

• Human's Knowledge (KH\mathbf{K}_HKH​): Comprehensive understanding of student learning styles and progress.

• AI's Knowledge (KA\mathbf{K}_AKA​): Basic algorithms for content recommendation without deep insights into individual student progress.

4. Wisdom (W): Human-AI Healthcare Decision-Making

   Scenario:

   A healthcare provider (Human) collaborates with an AI system (AI-Advisor) to make treatment decisions for patients.

   Incomplete Wisdom:

   Impact:

   The AI's lack of Wisdom in ethical considerations may suggest treatments that are medically effective but ethically questionable, such as ignoring patient consent.

   Remediation:

   The Human integrates ethical guidelines and patient-specific ethical considerations into the AI's Wisdom component, ensuring that treatment recommendations are both effective and ethically sound.

• Human's Wisdom (WH\mathbf{W}_HWH​): Ethical considerations and patient-specific factors.

• AI's Wisdom (WA\mathbf{W}_AWA​): Treatment efficacy data without ethical frameworks.

5. Purpose (P): Human-AI Urban Planning

   Scenario:

   Urban planners (Humans) utilize an AI system (AI-Plan) to design sustainable city layouts.

   Incomplete Purpose:

   Impact:

   The AI's lack of explicit Purpose related to sustainability leads to city layouts that are efficient in space but lack environmental and social sustainability.

   Remediation:

   The Humans redefine and communicate the AI's Purpose to prioritize sustainability, guiding the AI to generate urban plans that align with both space utilization and sustainable development goals.

• Human's Purpose (PH\mathbf{P}_HPH​): Sustainable and livable urban environments.

• AI's Purpose (PA\mathbf{P}_APA​): Maximizing space utilization without specific sustainability goals.

3.2 Inconsistent Input/Output (No-Inconsistent)

Definition:Inconsistent Input/Output arises when there are conflicting components (Data, Information, Knowledge, Wisdom, or Purpose) between stakeholders, leading to confusion and misunderstanding.

Examples Across DIKWP Components:

1. Data (D): Human-AI Environmental Monitoring

   Scenario:

   An environmental scientist (Human) and an AI system (AI-Monitor) receive Data from different sensor networks monitoring air quality.

   Inconsistent Data:

   Impact:

   The overlapping Data from sensors B and C may show discrepancies in pollutant measurements due to calibration differences, leading to inconsistent assessments of air quality.

   Remediation:

   Recalibrate sensors to ensure consistency, or implement data normalization techniques to reconcile differences in measurements, aligning the Data components.

• Human's Data (DH\mathbf{D}_HDH​): Pollutant levels from sensors A, B, and C.

• AI's Data (DA\mathbf{D}_ADA​): Pollutant levels from sensors B, C, and D.

2. Information (I): Human-AI Supply Chain Management

   Scenario:

   A supply chain manager (Human) and an AI system (AI-Optimizer) receive Information about supplier lead times from different sources.

   Inconsistent Information:

   Impact:

   Conflicting lead time information results in misaligned inventory planning, potentially causing delays or excess inventory.

   Remediation:

   Cross-verify supplier data sources, communicate with Supplier X to confirm accurate lead times, and update the Information components to resolve inconsistencies.

• Human's Information (IH\mathbf{I}_HIH​): Supplier X has a lead time of 2 weeks.

• AI's Information (IA\mathbf{I}_AIA​): Supplier X has a lead time of 3 weeks.

3. Knowledge (K): Human-AI Educational Platform

   Scenario:

   An educator (Human) and an AI-powered educational platform (AI-Edu) maintain Knowledge bases on teaching methodologies.

   Inconsistent Knowledge:

   Impact:

   The inconsistency in teaching methodologies leads to conflicting recommendations for curriculum development, confusing educators and students.

   Remediation:

   Align the Knowledge bases by integrating both interactive and traditional teaching methodologies, establishing a unified framework that balances various pedagogical approaches.

• Human's Knowledge (KH\mathbf{K}_HKH​): Emphasizes interactive and student-centered teaching methods.

• AI's Knowledge (KA\mathbf{K}_AKA​): Focuses on traditional, lecture-based teaching methods.

4. Wisdom (W): Human-AI Healthcare Decision-Making

   Scenario:

   A healthcare provider (Human) and an AI system (AI-Advisor) collaborate on patient treatment decisions.

   Inconsistent Wisdom:

   Impact:

   The AI's recommendations may overlook ethical considerations, leading to treatments that, while effective, may not align with patient values or consent.

   Remediation:

   Incorporate ethical frameworks and patient-centered considerations into the AI's Wisdom component, ensuring that treatment recommendations honor both efficacy and ethical standards.

• Human's Wisdom (WH\mathbf{W}_HWH​): Prioritizes patient autonomy and informed consent.

• AI's Wisdom (WA\mathbf{W}_AWA​): Focuses solely on treatment efficacy metrics.

5. Purpose (P): Human-AI Urban Planning

   Scenario:

   Urban planners (Humans) and an AI system (AI-Plan) collaborate on city layout designs.

   Inconsistent Purpose:

   Impact:

   The AI's focus on land use efficiency may conflict with the Humans' goals of sustainability and inclusivity, resulting in urban plans that are efficient but lack environmental and social considerations.

   Remediation:

   Realign the AI's Purpose to integrate sustainability and inclusivity goals, ensuring that urban planning efforts balance efficiency with broader societal objectives.

• Human's Purpose (PH\mathbf{P}_HPH​): Develops sustainable and inclusive urban environments.

• AI's Purpose (PA\mathbf{P}_APA​): Optimizes for maximum land use efficiency.

3.3 Imprecise Input/Output (No-Imprecise)

Definition:Imprecise Input/Output occurs when components (Data, Information, Knowledge, Wisdom, or Purpose) exchanged between stakeholders are vague or ambiguous, leading to misunderstandings and ineffective actions.

Examples Across DIKWP Components:

1. Data (D): Human-AI Environmental Monitoring

   Scenario:

   An environmental scientist (Human) and an AI system (AI-Monitor) exchange Data on pollutant levels.

   Imprecise Data:

   Impact:

   The Human's vague Data descriptions ("high" vs. "medium") lead to misinterpretation when integrating with the AI's precise measurements, resulting in inaccurate assessments of air quality.

   Remediation:

   Standardize Data reporting by using precise numerical measurements across both stakeholders, eliminating ambiguity and enhancing data integration.

• Human's Data (DH\mathbf{D}_HDH​): Pollutant levels reported as "high," "medium," or "low" without specific measurements.

• AI's Data (DA\mathbf{D}_ADA​): Exact numerical measurements in parts per million (ppm).

2. Information (I): Human-AI Supply Chain Management

   Scenario:

   A supply chain manager (Human) and an AI system (AI-Optimizer) exchange Information on demand forecasts.

   Imprecise Information:

   Impact:

   The Human's vague descriptions prevent the AI from accurately adjusting inventory levels, leading to potential overstocking or stockouts.

   Remediation:

   Implement precise communication protocols where demand forecasts include specific numerical projections, ensuring clarity and accuracy in inventory optimization.

• Human's Information (IH\mathbf{I}_HIH​): Demand forecasts are described as "likely to increase" without specifying by how much.

• AI's Information (IA\mathbf{I}_AIA​): Provides exact percentage increases in demand.

3. Knowledge (K): Human-AI Educational Platform

   Scenario:

   An educator (Human) and an AI-powered educational platform (AI-Edu) share Knowledge on teaching methodologies.

   Imprecise Knowledge:

   Impact:

   The Human's vague descriptions lead to misunderstandings when the AI attempts to implement or recommend teaching methods, reducing the effectiveness of educational strategies.

   Remediation:

   Enhance Knowledge exchange by providing detailed descriptions and specific examples of teaching methodologies, ensuring both stakeholders have a clear and comprehensive understanding.

• Human's Knowledge (KH\mathbf{K}_HKH​): Describes teaching methods as "engaging and effective" without detailing specific techniques.

• AI's Knowledge (KA\mathbf{K}_AKA​): Provides detailed descriptions of specific teaching techniques.

4. Wisdom (W): Human-AI Healthcare Decision-Making

   Scenario:

   A healthcare provider (Human) and an AI system (AI-Advisor) collaborate on treatment decisions.

   Imprecise Wisdom:

   Impact:

   The Human's vague advice leads to inconsistent application of ethical considerations, while the AI's rigid guidelines may not account for individual patient nuances, resulting in suboptimal treatment decisions.

   Remediation:

   Develop more precise Wisdom guidelines that include specific ethical principles and adaptable frameworks, enabling both stakeholders to make informed and contextually appropriate decisions.

• Human's Wisdom (WH\mathbf{W}_HWH​): Advises to "consider patient well-being," which is subjective.

• AI's Wisdom (WA\mathbf{W}_AWA​): Uses predefined ethical guidelines without contextual adaptability.

5. Purpose (P): Human-AI Urban Planning

   Scenario:

   Urban planners (Humans) and an AI system (AI-Plan) collaborate on city layout designs.

   Imprecise Purpose:

   Impact:

   The lack of precise Purpose definitions leads to misaligned design priorities, resulting in urban plans that may be efficient but fail to enhance the overall living environment.

   Remediation:

   Define and communicate specific Purpose criteria, such as sustainability metrics, social inclusivity standards, and quality of life indicators, to guide the AI in generating designs that align with comprehensive urban improvement goals.

• Human's Purpose (PH\mathbf{P}_HPH​): Aims to create "a better living environment" without specifying criteria.

• AI's Purpose (PA\mathbf{P}_APA​): Focuses on "efficient land use" without considering broader living environment aspects.

4. Summary of the 3-No Problems Across DIKWP Components

5. Remediation Strategies for the 3-No Problems Across DIKWP Components5.1 Remedied Connectivities (Addressing Incompleteness)

• Objective: Ensure comprehensive coverage of necessary DIKWP components to eliminate gaps in understanding or action.

• Mechanism:

1. Identify Missing Components: Analyze interactions to detect absent Data, Information, Knowledge, Wisdom, or Purpose elements.

2. Provide Missing Components: Supply the absent elements through data sharing, knowledge transfer, or clarifying purposes.

• Example Application:

  In the Human-AI Healthcare Decision-Making scenario, the Human identifies that the AI lacks patient-specific ethical considerations. By integrating detailed ethical guidelines and patient profiles into the AI's Knowledge and Wisdom components, the incompleteness is addressed, ensuring ethical and effective treatment recommendations.

5.2 Eliminating Inconsistencies

• Objective: Align conflicting DIKWP components to achieve coherence and prevent misunderstandings.

• Mechanism:

1. Quantify Inconsistency: Measure similarity or overlap between conflicting components using mathematical metrics (e.g., cosine similarity).

2. Reconcile Conflicts: Use averaging, dialogue, or standardization techniques to harmonize the conflicting components.

• Example Application:

  In the Human-AI Financial Forecasting scenario, conflicting market trend information is reconciled by verifying data sources and updating the Information components to reflect accurate and consistent market indicators, thereby aligning the stakeholders' understanding.

5.3 Reducing Imprecision

• Objective: Enhance the precision and clarity of DIKWP components to eliminate ambiguity and improve effectiveness.

• Mechanism:

1. Assess Precision: Evaluate the level of ambiguity or vagueness in components using entropy-based measures or clarity assessments.

2. Clarify Components: Provide detailed descriptions, specific measurements, and standardized definitions to reduce imprecision.

• Example Application:

  In the Human-AI Educational Tutoring scenario, the AI-Tutor enhances its Knowledge component by adding detailed semantic attributes and specific examples, thereby reducing imprecision and ensuring that the student receives clear and comprehensive explanations of calculus concepts.

6. Conclusion

The 3-No Problems—Incomplete Input/Output, Inconsistent Input/Output, and Imprecise Input/Output—are fundamental challenges within the DIKWP*DIKWP interaction framework that impede effective communication and mutual understanding between stakeholders. While initial mappings associated these problems specifically with Data, Information, and Knowledge, a comprehensive analysis reveals that these deficiencies are cross-cutting issues that can affect any of the DIKWP components—Data (D), Information (I), Knowledge (K), Wisdom (W), and Purpose (P)—depending on the interaction context.

Key Takeaways:

• Cross-Cutting Nature: The 3-No Problems are not confined to specific DIKWP components but are pervasive across the entire DIKWP framework.

• Contextual Impact: The manifestation and impact of these problems depend on the specific context and the components involved in the interaction.

• Flexible Remediation: Effective remediation requires a flexible approach tailored to the specific deficiencies and the DIKWP components they affect.

Future Directions:

Future research should explore the dynamic interplay of the 3-No Problems across the DIKWP components in diverse real-world scenarios. Developing advanced detection and remediation techniques that account for the cross-cutting nature of these communication deficiencies will enhance the robustness and effectiveness of human-AI collaborations.

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Acknowledgments

The author extends gratitude to Prof. Yucong Duan for his pioneering work on the DIKWP model and the Theory of Relativity of Consciousness, which have significantly influenced the conceptual framework of this analysis. Appreciation is also given to colleagues in cognitive science and artificial intelligence for their invaluable feedback and insights.

Author Information

Correspondence and requests for materials should be addressed to [Author's Name and Contact Information].

Keywords: DIKWP Model, 3-No Problems, Relativity of Hallucination, Human-AI Interaction, Cognitive Enclosure, Mathematical Framework, Data-Information-Knowledge-Wisdom-Purpose, Hallucination Mitigation, Understanding Enhancement