Safe and Ethical AI (SEA) Platform Network · Linking Artificial Intelligence Principles (LAIP)

We should adhere to the principles of fairness and non discrimination, and avoid biases and discrimination based on ethnicities, beliefs, nationalities, genders, etc., during the process of data collection, algorithm design, technology development, and product development and application.

Principle: Global AI Governance Initiative, October 18, 2023

Published by Cyberspace Administration of China

Related Principles

2. Fairness and Justice

The development of AI should promote fairness and justice, protect the rights and interests of all stakeholders, and promote equal opportunities. Through technology advancement and management improvement, prejudices and discriminations should be eliminated as much as possible in the process of data acquisition, algorithm design, technology development, and product development and application.

Published by National Governance Committee for the New Generation Artificial Intelligence, China in Governance Principles for the New Generation Artificial Intelligence--Developing Responsible Artificial Intelligence, Jun 17, 2019

Chapter 3. The Norms of Research and Development

　　10. Strengthen the awareness of self discipline. Strengthen self discipline in activities related to AI research and development, actively integrate AI ethics into every phase of technology research and development, consciously carry out self censorship, strengthen self management, and do not engage in AI research and development that violates ethics and morality. 　　11. Improve data quality. In the phases of data collection, storage, use, processing, transmission, provision, disclosure, etc., strictly abide by data related laws, standards and norms. Improve the completeness, timeliness, consistency, normativeness and accuracy of data. 　　12. Enhance safety, security and transparency. In the phases of algorithm design, implementation, and application, etc., improve transparency, interpretability, understandability, reliability, and controllability, enhance the resilience, adaptability, and the ability of anti interference of AI systems, and gradually realize verifiable, auditable, supervisable, traceable, predictable and trustworthy AI. 　　13. Avoid bias and discrimination. During the process of data collection and algorithm development, strengthen ethics review, fully consider the diversity of demands, avoid potential data and algorithmic bias, and strive to achieve inclusivity, fairness and non discrimination of AI systems.

Published by National Governance Committee for the New Generation Artificial Intelligence, China in Ethical Norms for the New Generation Artificial Intelligence, Sep 25, 2021

Principle 1 – Fairness

The fairness principle requires taking necessary actions to eliminate bias, discriminationor stigmatization of individuals, communities, or groups in the design, data, development, deployment and use of AI systems. Bias may occur due to data, representation or algorithms and could lead to discrimination against the historically disadvantaged groups. When designing, selecting, and developing AI systems, it is essential to ensure just, fair, non biased, non discriminatory and objective standards that are inclusive, diverse, and representative of all or targeted segments of society. The functionality of an AI system should not be limited to a specific group based on gender, race, religion, disability, age, or sexual orientation. In addition, the potential risks, overall benefits, and purpose of utilizing sensitive personal data should be well motivated and defined or articulated by the AI System Owner. To ensure consistent AI systems that are based on fairness and inclusiveness, AI systems should be trained on data that are cleansed from bias and is representative of affected minority groups. Al algorithms should be built and developed in a manner that makes their composition free from bias and correlation fallacy.

Published by SDAIA in AI Ethics Principles, Sept 14, 2022

· Plan and Design:

The fairness principle requires taking necessary actions to eliminate bias, discrimination or stigmatization of individuals, communities, or groups in the design, data, development, deployment and use of AI systems. Bias may occur due to data, representation or algorithms and could lead to discrimination against the historically disadvantaged groups. When designing, selecting, and developing AI systems, it is essential to ensure just, fair,non biased, non discriminatory and objective standards that are inclusive, diverse, andrepresentative of all or targeted segments of society. The functionality of an AI system shouldnot be limited to a specific group based on gender, race, religion, disability, age, or sexualorientation. In addition, the potential risks, overall benefits, and purpose of utilizing sensitivepersonal data should be well motivated and defined or articulated by the AI System Owner. To ensure consistent AI systems that are based on fairness and inclusiveness, AI systems shouldbe trained on data that are cleansed from bias and is representative of affected minority groups.Al algorithms should be built and developed in a manner that makes their composition free frombias and correlation fallacy.

Published by SDAIA in AI Ethics Principles, Sept 14, 2022

· 2) Diversity and Fairness:

Artificial intelligence should provide non discriminatory services to various groups of people in accordance with the principles of fairness, equity and inclusion. We aim to initiate from the disciplined approach of system engineering, and to construct the AI system with diverse data and unbiased algorithms, thus improving the fairness of user experience.

Published by Youth Work Committee of Shanghai Computer Society in Chinese Young Scientists’ Declaration on the Governance and Innovation of Artificial Intelligence, Aug 29, 2019