World Artificial Consciousness Association (WACA)and the International Artificial Intelligence DIKWP Evaluation Standards Committee (DIKWP-SC)

I. Background of the Organization: World Artificial Consciousness Association (WACA)1. Organizational Positioning

The World Artificial Consciousness Association (WACA) is a high-level international organization that integrates academic research and industry. It not only focuses on traditional weak AI but also devotes itself to exploring higher-level, “quasi-conscious” forms of AI (Strong AI or even Artificial Consciousness). WACA brings together leading experts in multiple disciplines—such as intelligent science, neuroscience, cognitive science, ethics, and computer science—from around the world to build a cross-regional, cross-disciplinary, and cross-industry platform for AI research and applications.

2. Mission of the Association

• Advancing “Artificial Consciousness” ResearchActively promoting research in areas such as brain-inspired intelligence, cognitive computing, and neuromorphic chips, driving AI development toward systems with autonomous “intent,” higher cognitive abilities, and advanced deep learning capabilities.

• Encouraging Multidisciplinary IntegrationWelcoming experts and institutions from neuroscience, philosophy, ethics, sociology, law, economics, and other fields to expand both the breadth and depth of AI research.

• Facilitating AI and Society’s Coordinated DevelopmentCollaborating with governments, enterprises, and NGOs to ensure that AI development aligns with security, sustainability, compliance, and humanistic considerations.

• Building a Global Exchange PlatformHosting international conferences, workshops, competitions, and collaborative projects to strengthen academic and industrial interaction globally, and to actively promote international standardization and evaluation systems.

II. Introduction to the DIKWP Model

DIKWP is a comprehensive evaluation model initiated by WACA and promoted by the “International AI DIKWP Standards Committee,” designed to evaluate AI from data input to the final decision-making purpose. The name derives from five key elements:

1. Data

• Covers data acquisition and collection, data governance and quality control, data security, compliance, privacy protection, etc.

• Emphasizes the legality, accuracy, diversity of data sources, as well as handling imbalance and bias in data.

2. Intelligence

• Focuses on machine learning algorithms, deep learning models, multimodal fusion, reasoning, and learning efficiency.

• Includes advanced technologies such as adversarial defense, online learning, transfer learning, and federated learning.

3. Knowledge

• Involves knowledge acquisition, knowledge representation, knowledge graphs, and knowledge reasoning.

• Explores how knowledge is accumulated and transformed from “data,” as well as how to integrate human expert experience or external knowledge bases to enhance algorithm explainability and versatility.

4. Wisdom

• Stresses higher cognition and contextual understanding, holistic decision-making, cross-domain applications, and real-time decision-making.

• Balances timeliness, robustness, security, credibility, and human-machine collaboration as comprehensive indicators.

5. Purpose

• Includes ethical and moral considerations, legal and regulatory requirements, sustainable development goals, and social value assessment.

• Pays special attention to why an AI makes certain decisions—i.e., the goal-setting and intent design process—and the impact on society, ensuring alignment with shared human interests and core values.

As a novel AI evaluation framework, DIKWP provides academia and industry with a comprehensive lifecycle assessment approach, ensuring security and compliance while facilitating the continuous upgrading and evolution of AI.

III. The International Artificial Intelligence DIKWP Evaluation Standards Committee (DIKWP-SC): Main Responsibilities

To promote the global adoption and standardization of the DIKWP model, WACA has founded the “International Artificial Intelligence DIKWP Evaluation Standards Committee (DIKWP-SC)” The Committee’s main responsibilities include:

1. Standards and Norms Development

• Organizing experts worldwide to study the metrics, classifications, and evaluation methodologies within the DIKWP framework, formulating practical and internationally recognized technical standards and industry guidelines.

• Developing specialized sub-standards and use-case specifications for different industries (e.g., healthcare, finance, manufacturing, urban governance).

2. Evaluation System Enhancement

• Compiling a complete set of DIKWP evaluation protocols, including operational manuals and data guidelines, to facilitate implementation by governments, enterprises, and research institutes.

• Tracking AI technology frontiers and promptly revising, upgrading, and expanding evaluation metrics and methods to keep pace with technological advances.

3. Consulting and Training

• Providing DIKWP-based consulting services for governments, social organizations, and enterprises, including technical evaluation, system acceptance, risk monitoring, and technical planning.

• Offering specialized training and certification to cultivate skilled evaluators and promote international communication in the AI evaluation field.

4. Scientific Research Collaboration and Outcomes Sharing

• Hosting international academic conferences, high-level forums, workshops, and competitions to inspire research and best practices for each DIKWP dimension among global institutions, enterprises, and universities.

• Collaborating with standardization bodies (e.g., ISO, IEEE, IETF) to integrate existing standards with the DIKWP model for mutual benefit.

5. Cross-Disciplinary and Cross-Industry Advancement

• Addressing the complexities of AI in regulatory, ethical, and legal contexts by involving experts from law, social sciences, and industry, thereby strengthening the “Purpose” dimension.

• Setting up working groups for key areas such as smart healthcare, smart cities, smart industry, and intelligent transportation to define industry-specific standardization and application frameworks.

IV. Core Committee Members (Name and Affiliation Only)

(Listed in the order they appeared in the original data.)

1. Duan Yucong - World Artificial Consciousness Association (Chairman)

2. Dou Erxiang - Peking University

3. Gao Musheng - Shanghai Nichong Burui Intelligent Technology Co., Ltd.

4. Han Long - Hainan University

5. Jiang Zuo-wen - Ningbo University

6. Jiang Binxiang - Shandong University

7. Li Sheng - Guangxi Normal University

8. Chen Shiping - Commonwealth Scientific and Industrial Research Organisation (CSIRO), Australia (International Academician)

9. Weng Jialiang - AIII Artificial Intelligence International Institute

10. Sajid Anwar - Institute of Management Sciences, Peshawar (Pakistan)

11. Li Chunguo - Southeast University (International Academician)

12. Liu Yongmou - Renmin University of China

13. Yu Lei - Inner Mongolia University

14. Qiu Jiawen - Kenside (Zhuhai) Co., Ltd.

15. Huang Qibao - Shangrao Normal University

16. Song Zhengyang - Shanghai Pudong Development Bank

17. Sun Qiang - Xi’an University of Technology

18. Wen Bin - Hainan Normal University

19. Cheng Hexiang - Southwest University of Political Science and Law

20. Xu Yongshun - Jiangsu Second Normal University

21. Zhang Jinsong - Beijing Research Institute of Standardization

22. Zhou Dexing - The Second Affiliated Hospital of Hainan Medical University

23. Yao Xifan - South China University of Technology (Committee Chairman)

24. Xu Caiguo - Ningbo University

25. Wu Aiqun - Shanghai Aerospace Information Technology Research Institute

26. Wang Peng - Beijing Academy of Social Sciences

27. Wang Lei - Hainan University

28. Wang Jun - Jiangsu Lizhuo Information Technology Co., Ltd.

29. Wang Jinlong - Tsinghua University Institute

30. Tong Dawei - CETC Investment

31. Song Jia - Chinese Academy of Tropical Agricultural Sciences

32. Shang Delong - Institute of Microelectronics, Chinese Academy of Sciences

33. Liu Yanfei - Chongqing Police College

34. Liu Minglei - Mechatronics Association

35. Li Yingbo - Blue (France)

36. Han Wei - Eurasian Academy of Sciences (China), Guangdong-Hong Kong-Macao Greater Bay Area Computing Power Economy Research Institute

37. Gu Yanhui - Huaiyin Institute of Technology

38. Dong Liang - Tencent HunYuan Model

39. Chen Liang - Hainan Meteorological Bureau

40. Che Haoyang - Zeekr Automobile

41. Fan Huiwen - SK China

42. Liu Bin - Northeast Petroleum University

43. Yan Baoping - Nanjing University of the Arts

44. Wang Changquan - Beijing Vocational College of Labor and Social Security

45. He Hui - Harbin Institute of Technology

46. Huang Yong - Guangdong University of Science and Technology

47. Andrea Baldini - Nanjing University

48. Peng Junhui - Beijing XiaDi Robotics Technology Co., Ltd.

49. Xiong Xi - Chengdu University of Information Technology

50. Liu Hongyang - Kyung Hee University, Advanced Information Technology Research Center (CAlTech), Korea

51. Liu Chunguo - Shandong Foreign Affairs Vocational University

52. Liu Yiming - Chinese Academy of Sciences Network Information Center (Affiliated Company)

53. Chen Guilin - Guangdong Advanced Institute of Science and Technology

54. Yang Minghao - Hainan Universal Intelligence Technology Co., Ltd.

55. Zou Jun - Hainan Women and Children’s Medical Center

56. Pu Yifei - Sichuan University

57. Wang Xinsheng - Harbin Institute of Technology (Weihai Campus)

58. Ji Hailiang - (Independent Consultant / No Fixed Institution)

59. Liu Haiping - Hubei Engineering College

60. Zhou Xiangyong - The Second Affiliated Hospital of Zhejiang University School of Medicine

61. Liu Zelong - China-Japan Friendship Hospital

62. Yan Ziye - Guangzhou Bys Medical Technology Co., Ltd.

63. Zhang Sheng - The First Affiliated Hospital of Soochow University

64. Yin Sheng - Zhongnan University of Economics and Law

65. Guo Jianan - Asian Institute of Technology

66. Wang Zumin - Dalian University

67. Hu Chunqiang - Chongqing University

68. Pan Zhifang - Wenzhou Medical University

69. Liu Luyi - Leshan Normal University

70. Xing Hongliang - Shenyang Aerospace University

71. Cao Quanlai - Changzhou University

72. Wei Wei - Xi’an University of Technology

73. Sun Chen - China Electronics Standardization Institute under the Ministry of Industry and Information Technology (MIIT)

74. Xu Jingheng - Sangfor Technologies / Shenzhen Municipal Key Laboratory of Cloud Security

75. Li Xiaojun - CEC New Smart City Research Institute Co., Ltd.

76. Huang Jianqiang - China Telecom Hainan Branch

77. He Changxu - Shanghai Huacai Group

78. Zhang QuanGuo - Henan Agricultural University (International Academician)

79. Liu Jun - Chengdu University of Information Technology

80. Liu Hongjian - Sino-American Silicon Valley Development Promotion Association Innovation Technology Industrialization Research Institute

81. Dai Yunhai - Sungkyunkwan University

82. Zhang Bin - Xinjiang Political Science and Law College Information Network Security Department, Network Information Center

83. Wang Qiaohua - International Medical University, USA (International Academician)

84. Sun Qiang - Xi’an University of Technology

85. Peng Ling - Huanggang Real Estate Registration Center

86. Li Lizhong - Quanshi International (International Academician)

87. An Xiaomi - Renmin University of China

88. Wang Donghai - China Electronics Technology Group

89. Ren Qilong - BAIC Foton Motor (International Academician)

90. Zhu Mianmao - Hainan Open University

91. Liu Zhen - Nagasaki University of Applied Sciences (Japan Engineering Academy Academician)

92. Dai Jianhua - Hunan Normal University

93. Xia Qinghua - Zhejiang University

94. Jiang Kun - Aizu University

95. Zhang Jicong - Beihang University

96. Jin Zhuo - Royal Society of Arts & Crafts, UK (International Academician)

97. Feng Zaiwen - Huazhong Agricultural University

98. Jiang Linhua - Bolivian Academy of Sciences (International Academician)

99. Wang He - Guangzhou University Architectural Design Institute (International Academician)

100. Wang Yongzhi - Xi’an University of Architecture and Technology

101. Zhang Yingsheng - China Scientific and Technological Information Institute

102. Qu Xilong - Changsha Normal University

103. Liao Hong - Hongshang Group (International Academician)

104. Wu Dongfang - Zhejiang University (International Academician)

105. Meng Lin - Ritsumeikan University

106. Wang Huaping - Sun Yat-sen University

107. Ning Huansheng - University of Science and Technology Beijing (International Academician)

108. Jiao Li Cheng - Xi’an University of Electronic Science and Technology (International Academician)

109. Hu Junhong - Beijing Normal University

110. Cai Hengjin - Wuhan University (International Academician)

111. Jin Chaohui - Hunan University of Chinese Medicine (International Academician)

112. He Yigang - Wuhan University (International Academician)

113. Zhu Wenhua - Asia-Pacific Academy of Sciences (International Academician)

114. Li Jie - Japan Engineering Academy (International Academician)

115. Zhao Xiaoliang - Zhejiang University

116. Ren Chengxiang - University of Science and Technology Beijing

117. Gong Jiayuan - Hubei Automotive Industry College

V. Future Prospects

1. Standardization and Deepening International Collaboration

• The Committee will establish strategic partnerships with international standardization organizations (ISO, IEEE, IETF), government agencies, industry alliances, and NGOs, striving to integrate DIKWP into policy frameworks worldwide, ultimately forming an authoritative standard with global influence.

2. Extensive Application Across Multiple Scenarios and Industries

• The Committee plans to explore the diverse needs of AI in healthcare, finance, public safety, energy, manufacturing, education, agriculture, and environmental protection. By adapting DIKWP to each scenario, the Committee will develop industry standards and application guidelines that maximize the synergy of AI technologies with real-world use cases.

3. Talent Cultivation and Technology Accumulation

• Through international competitions, forums, workshops, and certification programs, the Committee aims to train professionals in the field of AI evaluation. Supported by academic and industry resources, they will build AI algorithm libraries, benchmarking datasets, and open-source tools to sustain the long-term development of DIKWP evaluation.

4. Ethics and Social Responsibility

• Recognizing the growing impact of AI in areas such as defense, security, finance, and healthcare, the Committee will reinforce the “Purpose” dimension, with particular attention to environmental protection, social justice, sustainability, and ESG topics, ensuring the practice of “Responsible AI.”

5. Ongoing Technological Upgrades and Model Refinement

• Since AI technologies evolve rapidly—large language models, multimodal models, new neural-network chips, etc.—the DIKWP model also requires continual updates to incorporate cutting-edge research and address challenges like adversarial attacks and algorithmic security, ensuring its forward-looking and practical relevance.

Through these strategies, the World Artificial Consciousness Association and its International Artificial Intelligence DIKWP Evaluation Standards Committee (DIKWP-SC) will actively promote “Data–Intelligence–Knowledge–Wisdom–Purpose” as the five pillars of AI evaluation and standardization on a global scale, enhancing the credibility, security, compliance, and sustainability of the AI industry while fostering the coordinated advancement of technology innovation and human welfare.

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