Linking Artificial Intelligence Principles (LAIP)

· 2) Humanistic approach:

Humanistic approach: Artificial intelligence should empower users to make their own decisions. We are committed to providing transparent, understandable decision interpretations and interactive tools, allowing users to join, monitor or involve in the decision making process.

Principle: Chinese Young Scientists’ Declaration on the Governance and Innovation of Artificial Intelligence, Aug 29, 2019

Published by Youth Work Committee of Shanghai Computer Society

Related Principles

(Preamble)

Automated decision making algorithms are now used throughout industry and government, underpinning many processes from dynamic pricing to employment practices to criminal sentencing. Given that such algorithmically informed decisions have the potential for significant societal impact, the goal of this document is to help developers and product managers design and implement algorithmic systems in publicly accountable ways. Accountability in this context includes an obligation to report, explain, or justify algorithmic decision making as well as mitigate any negative social impacts or potential harms. We begin by outlining five equally important guiding principles that follow from this premise: Algorithms and the data that drive them are designed and created by people There is always a human ultimately responsible for decisions made or informed by an algorithm. "The algorithm did it" is not an acceptable excuse if algorithmic systems make mistakes or have undesired consequences, including from machine learning processes.

Published by Fairness, Accountability, and Transparency in Machine Learning (FAT/ML) in Principles for Accountable Algorithms, Jul 22, 2016 (unconfirmed)

• Require Accountability for Ethical Design and Implementation

The social implications of computing have grown and will continue to expand as more people have access to implementations of AI. Public policy should work to identify and mitigate discrimination caused by the use of AI and encourage designing in protections against these harms. [Recommendations] • Standing for “Accountable Artificial Intelligence”: Governments, industry and academia should apply the Information Accountability Foundation’s principles to AI. Organizations implementing AI solutions should be able to demonstrate to regulators that they have the right processes, policies and resources in place to meet those principles. • Transparent decisions: Governments should determine which AI implementations require algorithm explainability to mitigate discrimination and harm to individuals.

Published by Intel in AI public policy principles, Oct 18, 2017

8. Principle of user assistance

Developers should take it into consideration that AI systems will support users and make it possible to give them opportunities for choice in appropriate manners. [Comment] In order to support users of AI systems, it is recommended that developers pay attention to the followings: ● To make efforts to make available interfaces that provide in a timely and appropriate manner the information that can help users’ decisions and are easy to use for them. ● To make efforts to give consideration to make available functions that provide users with opportunities for choice in a timely and appropriate manner (e.g., default settings, easy to understand options, feedbacks, emergency warnings, handling of errors, etc.). And ● To make efforts to take measures to make AI systems easier to use for socially vulnerable people such as universal design. In addition, it is recommended that developers make efforts to provide users with appropriate information considering the possibility of changes in outputs or programs as a result of learning or other methods of AI systems.

Published by Ministry of Internal Affairs and Communications (MIC), the Government of Japan in AI R&D Principles, Jul 28, 2017

2 RESPECT FOR AUTONOMY PRINCIPLE

AIS must be developed and used while respecting people’s autonomy, and with the goal of increasing people’s control over their lives and their surroundings. 1) AIS must allow individuals to fulﬁll their own moral objectives and their conception of a life worth living. 2) AIS must not be developed or used to impose a particular lifestyle on individuals, whether directly or indirectly, by implementing oppressive surveillance and evaluation or incentive mechanisms. 3) Public institutions must not use AIS to promote or discredit a particular conception of the good life. 4) It is crucial to empower citizens regarding digital technologies by ensuring access to the relevant forms of knowledge, promoting the learning of fundamental skills (digital and media literacy), and fostering the development of critical thinking. 5) AIS must not be developed to spread untrustworthy information, lies, or propaganda, and should be designed with a view to containing their dissemination. 6) The development of AIS must avoid creating dependencies through attention capturing techniques or the imitation of human characteristics (appearance, voice, etc.) in ways that could cause confusion between AIS and humans.

Published by University of Montreal in The Montreal Declaration for a Responsible Development of Artificial Intelligence, Dec 4, 2018

10. Responsibility, accountability and transparency

a. Build trust by ensuring that designers and operators are responsible and accountable for their systems, applications and algorithms, and to ensure that such systems, applications and algorithms operate in a transparent and fair manner. b. To make available externally visible and impartial avenues of redress for adverse individual or societal effects of an algorithmic decision system, and to designate a role to a person or office who is responsible for the timely remedy of such issues. c. Incorporate downstream measures and processes for users or stakeholders to verify how and when AI technology is being applied. d. To keep detailed records of design processes and decision making.

Published by Personal Data Protection Commission (PDPC), Singapore in A compilation of existing AI ethical principles (Annex A), Jan 21, 2020