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

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.

Principle: Universal Guidelines for Artificial Intelligence, Oct 23, 2018

Published by The Public Voice coalition, established by Electronic Privacy Information Center (EPIC)

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

Fairness Obligation

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 Center for AI and Digital Policy in Universal Guidelines for AI, Oct, 2018

· Fairness and inclusion

AI systems should make the same recommendations for everyone with similar characteristics or qualifications. Employers should be required to test AI in the workplace on a regular basis to ensure that the system is built for purpose and is not harmfully influenced by bias of any kind — gender, race, sexual orientation, age, religion, income, family status and so on. AI should adopt inclusive design efforts to anticipate any potential deployment issues that could unintentionally exclude people. Workplace AI should be tested to ensure that it does not discriminate against vulnerable individuals or communities. Governments should review the impact of workplace, governmental and social AI on the opportunities and rights of poor people, Indigenous peoples and vulnerable members of society. In particular, the impact of overlapping AI systems toward profiling and marginalization should be identified and countered.

Published by Centre for International Governance Innovation (CIGI), Canada in Toward a G20 Framework for Artificial Intelligence in the Workplace, Jul 19, 2018

· Transparency

As AI increasingly changes the nature of work, workers, customers and vendors need to have information about how AI systems operate so that they can understand how decisions are made. Their involvement will help to identify potential bias, errors and unintended outcomes. Transparency is not necessarily nor only a question of open source code. While in some circumstances open source code will be helpful, what is more important are clear, complete and testable explanations of what the system is doing and why. Intellectual property, and sometimes even cyber security, is rewarded by a lack of transparency. Innovation generally, including in algorithms, is a value that should be encouraged. How, then, are these competing values to be balanced? One possibility is to require algorithmic verifiability rather than full algorithmic disclosure. Algorithmic verifiability would require companies to disclose not the actual code driving the algorithm but information allowing the effect of their algorithms to be independently assessed. In the absence of transparency regarding their algorithms’ purpose and actual effect, it is impossible to ensure that competition, labour, workplace safety, privacy and liability laws are being upheld. When accidents occur, the AI and related data will need to be transparent and accountable to an accident investigator, so that the process that led to the accident can be understood.

Published by Centre for International Governance Innovation (CIGI), Canada in Toward a G20 Framework for Artificial Intelligence in the Workplace, Jul 19, 2018

· Proportionality and Do No Harm

25. It should be recognized that AI technologies do not necessarily, per se, ensure human and environmental and ecosystem flourishing. Furthermore, none of the processes related to the AI system life cycle shall exceed what is necessary to achieve legitimate aims or objectives and should be appropriate to the context. In the event of possible occurrence of any harm to human beings, human rights and fundamental freedoms, communities and society at large or the environment and ecosystems, the implementation of procedures for risk assessment and the adoption of measures in order to preclude the occurrence of such harm should be ensured. 26. The choice to use AI systems and which AI method to use should be justified in the following ways: (a) the AI method chosen should be appropriate and proportional to achieve a given legitimate aim; (b) the AI method chosen should not infringe upon the foundational values captured in this document, in particular, its use must not violate or abuse human rights; and (c) the AI method should be appropriate to the context and should be based on rigorous scientific foundations. In scenarios where decisions are understood to have an impact that is irreversible or difficult to reverse or may involve life and death decisions, final human determination should apply. In particular, AI systems should not be used for social scoring or mass surveillance purposes.

Published by The United Nations Educational, Scientific and Cultural Organization (UNESCO) in The Recommendation on the Ethics of Artificial Intelligence, Nov 24, 2021