4. Fairness:

AI should be designed to minimize bias and promote inclusive representation.
Principle: Everyday Ethics for Artificial Intelligence: Five Areas of Ethical Focus, Sep 6, 2018

Published by IBM

Related Principles

(b) Inclusiveness:

AI should be inclusive, aiming to avoid bias and allowing for diversity and avoiding a new digital divide.

Published by The Extended Working Group on Ethics of Artificial Intelligence (AI) of the World Commission on the Ethics of Scientific Knowledge and Technology (COMEST), UNESCO in Suggested generic principles for the development, implementation and use of AI, Mar 21, 2019

4 Fairness and Non discrimination

Organisations that develop, deploy or use AI systems and any national laws that regulate such use shall ensure the non discrimination of AI outcomes, and shall promote appropriate and effective measures to safeguard fairness in AI use.

Published by International Technology Law Association (ITechLaw) in The Eight Principles of Responsible AI, May 23, 2019

3. Inclusion and Sharing

AI should promote green development to meet the requirements of environmental friendliness and resource conservation; AI should promote coordinated development by promoting the transformation and upgrading of all industries, and by narrowing regional disparities; AI should promote inclusive development through better education and training, support to the vulnerable groups to adapt, and efforts to eliminate digital divide; AI should promote shared development by avoiding data and platform monopolies, and promoting open and fair competition.

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

· We will make AI systems fair

1. Data ingested should, where possible, be representative of the affected population 2. Algorithms should avoid non operational bias 3. Steps should be taken to mitigate and disclose the biases inherent in datasets 4. Significant decisions should be provably fair

Published by Smart Dubai in Dubai's AI Principles, Jan 08, 2019

8. Fair and equal

We aspire to embed the principles of fairness and equality in datasets and algorithms applied in all phases of AI design, implementation, testing and usage – fostering fairness and diversity and avoiding unfair bias both at the input and output levels of AI.

Published by Telia Company AB in Telia Company Guiding Principles on trusted AI ethics, Jan 22, 2019