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

· 6. A.I. must guard against bias, ensuring proper, and representative research so that the wrong heuristics cannot be used to discriminate.

Principle: 10 AI rules, Jun 28, 2016

Published by Satya Nadella, CEO of Microsoft

Related Principles

Proportionality and harmlessness.

It should be recognised that AI technologies do not necessarily, in and of themselves, guarantee the prosperity of humans or the environment and ecosystems. In the event that any harm to humans may occur, risk assessment procedures should be applied and measures taken to prevent such harm from occurring. In other words, for a human person to be legally responsible for the decisions he or she makes to carry out one or more actions, there must be discernment (full human mental faculties), intention (human drive or desire) and freedom (to act in a calculated and premeditated manner). Therefore, to avoid falling into anthropomorphisms that could hinder eventual regulations and or wrong attributions, it is important to establish the conception of artificial intelligences as artifices, that is, as technology, a thing, an artificial means to achieve human objectives but which should not be confused with a human person. That is, the algorithm can execute, but the decision must necessarily fall on the person and therefore, so must the responsibility. Consequently, it emerges that an algorithm does not possess self determination and or agency to make decisions freely (although many times in colloquial language the concept of "decision" is used to describe a classification executed by an algorithm after training), and therefore it cannot be held responsible for the actions that are executed through said algorithm in question.

Published by OFFICE OF THE CHIEF OF MINISTERS UNDERSECRETARY OF INFORMATION TECHNOLOGIES in Recommendations for reliable artificial intelligence, Jnue 2, 2023

Responsible Deployment

Principle: The capacity of an AI agent to act autonomously, and to adapt its behavior over time without human direction, calls for significant safety checks before deployment, and ongoing monitoring. Recommendations: Humans must be in control: Any autonomous system must allow for a human to interrupt an activity or shutdown the system (an “off switch”). There may also be a need to incorporate human checks on new decision making strategies in AI system design, especially where the risk to human life and safety is great. Make safety a priority: Any deployment of an autonomous system should be extensively tested beforehand to ensure the AI agent’s safe interaction with its environment (digital or physical) and that it functions as intended. Autonomous systems should be monitored while in operation, and updated or corrected as needed. Privacy is key: AI systems must be data responsible. They should use only what they need and delete it when it is no longer needed (“data minimization”). They should encrypt data in transit and at rest, and restrict access to authorized persons (“access control”). AI systems should only collect, use, share and store data in accordance with privacy and personal data laws and best practices. Think before you act: Careful thought should be given to the instructions and data provided to AI systems. AI systems should not be trained with data that is biased, inaccurate, incomplete or misleading. If they are connected, they must be secured: AI systems that are connected to the Internet should be secured not only for their protection, but also to protect the Internet from malfunctioning or malware infected AI systems that could become the next generation of botnets. High standards of device, system and network security should be applied. Responsible disclosure: Security researchers acting in good faith should be able to responsibly test the security of AI systems without fear of prosecution or other legal action. At the same time, researchers and others who discover security vulnerabilities or other design flaws should responsibly disclose their findings to those who are in the best position to fix the problem.

Published by Internet Society, "Artificial Intelligence and Machine Learning: Policy Paper" in Guiding Principles and Recommendations, Apr 18, 2017

4. Fairness

Members of the JSAI will always be fair. Members of the JSAI will acknowledge that the use of AI may bring about additional inequality and discrimination in society which did not exist before, and will not be biased when developing AI. Members of the JSAI will, to the best of their ability, ensure that AI is developed as a resource that can be used by humanity in a fair and equal manner.

Published by The Japanese Society for Artificial Intelligence (JSAI) in The Japanese Society for Artificial Intelligence Ethical Guidelines, Feb 28, 2017

5 DEMOCRATIC PARTICIPATION PRINCIPLE

AIS must meet intelligibility, justiﬁability, and accessibility criteria, and must be subjected to democratic scrutiny, debate, and control. 1) AIS processes that make decisions affecting a person’s life, quality of life, or reputation must be intelligible to their creators. 2) The decisions made by AIS affecting a person’s life, quality of life, or reputation should always be justiﬁable in a language that is understood by the people who use them or who are subjected to the consequences of their use. Justiﬁcation consists in making transparent the most important factors and parameters shaping the decision, and should take the same form as the justiﬁcation we would demand of a human making the same kind of decision. 3) The code for algorithms, whether public or private, must always be accessible to the relevant public authorities and stakeholders for veriﬁcation and control purposes. 4) The discovery of AIS operating errors, unexpected or undesirable effects, security breaches, and data leaks must imperatively be reported to the relevant public authorities, stakeholders, and those affected by the situation. 5) In accordance with the transparency requirement for public decisions, the code for decision making algorithms used by public authorities must be accessible to all, with the exception of algorithms that present a high risk of serious danger if misused. 6) For public AIS that have a signiﬁcant impact on the life of citizens, citizens should have the opportunity and skills to deliberate on the social parameters of these AIS, their objectives, and the limits of their use. 7) We must at all times be able to verify that AIS are doing what they were programmed for and what they are used for. 8) Any person using a service should know if a decision concerning them or affecting them was made by an AIS. 9) Any user of a service employing chatbots should be able to easily identify whether they are interacting with an AIS or a real person. 10) Artiﬁcial intelligence research should remain open and accessible to all.

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

4. Fairness Obligation.

Institutions must ensure that AI systems do not reflect unfair bias or make impermissible discriminatory decisions. [Explanatory Memorandum] The Fairness Obligation recognizes that all automated systems make decisions that reflect bias and discrimination, but such decisions should not be normatively unfair. There is no simple answer to the question as to what is unfair or impermissible. The evaluation often depends on context. But the Fairness Obligation makes clear that an assessment of objective outcomes alone is not sufficient to evaluate an AI system. Normative consequences must be assessed, including those that preexist or may be amplified by an AI system.

Published by The Public Voice coalition, established by Electronic Privacy Information Center (EPIC) in Universal Guidelines for Artificial Intelligence, Oct 23, 2018