Guard against creating or reinforcing bias

Principle: GE Healthcare AI principles, Oct 1, 2018 (unconfirmed)

Published by GE Healthcare

Related Principles

· Article 3: Fair and just.

The development of artificial intelligence should ensure fairness and justice, avoid bias or discrimination against specific groups or individuals, and avoid placing disadvantaged people in an even more unfavorable position.

Published by Artificial Intelligence Industry Alliance (AIIA), China in Joint Pledge on Artificial Intelligence Industry Self-Discipline (Draft for Comment), May 31, 2019

· 2. Avoid creating or reinforcing unfair bias.

AI algorithms and datasets can reflect, reinforce, or reduce unfair biases. We recognize that distinguishing fair from unfair biases is not always simple, and differs across cultures and societies. We will seek to avoid unjust impacts on people, particularly those related to sensitive characteristics such as race, ethnicity, gender, nationality, income, sexual orientation, ability, and political or religious belief.

Published by Google in Artificial Intelligence at Google: Our Principles, Jun 7, 2018

· 5. Non Discrimination

Discrimination concerns the variability of AI results between individuals or groups of people based on the exploitation of differences in their characteristics that can be considered either intentionally or unintentionally (such as ethnicity, gender, sexual orientation or age), which may negatively impact such individuals or groups. Direct or indirect discrimination through the use of AI can serve to exploit prejudice and marginalise certain groups. Those in control of algorithms may intentionally try to achieve unfair, discriminatory, or biased outcomes in order to exclude certain groups of persons. Intentional harm can, for instance, be achieved by explicit manipulation of the data to exclude certain groups. Harm may also result from exploitation of consumer biases or unfair competition, such as homogenisation of prices by means of collusion or non transparent market. Discrimination in an AI context can occur unintentionally due to, for example, problems with data such as bias, incompleteness and bad governance models. Machine learning algorithms identify patterns or regularities in data, and will therefore also follow the patterns resulting from biased and or incomplete data sets. An incomplete data set may not reflect the target group it is intended to represent. While it might be possible to remove clearly identifiable and unwanted bias when collecting data, data always carries some kind of bias. Therefore, the upstream identification of possible bias, which later can be rectified, is important to build in to the development of AI. Moreover, it is important to acknowledge that AI technology can be employed to identify this inherent bias, and hence to support awareness training on our own inherent bias. Accordingly, it can also assist us in making less biased decisions.

Published by The European Commission’s High-Level Expert Group on Artificial Intelligence in Draft Ethics Guidelines for Trustworthy AI, Dec 18, 2018


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

Fourth principle: Bias and Harm Mitigation

Those responsible for AI enabled systems must proactively mitigate the risk of unexpected or unintended biases or harms resulting from these systems, whether through their original rollout, or as they learn, change or are redeployed. AI enabled systems offer significant benefits for Defence. However, the use of AI enabled systems may also cause harms (beyond those already accepted under existing ethical and legal frameworks) to those using them or affected by their deployment. These may range from harms caused by a lack of suitable privacy for personal data, to unintended military harms due to system unpredictability. Such harms may change over time as systems learn and evolve, or as they are deployed beyond their original setting. Of particular concern is the risk of discriminatory outcomes resulting from algorithmic bias or skewed data sets. Defence must ensure that its AI enabled systems do not result in unfair bias or discrimination, in line with the MOD’s ongoing strategies for diversity and inclusion. A principle of bias and harm mitigation requires the assessment and, wherever possible, the mitigation of these biases or harms. This includes addressing bias in algorithmic decision making, carefully curating and managing datasets, setting safeguards and performance thresholds throughout the system lifecycle, managing environmental effects, and applying strict development criteria for new systems, or existing systems being applied to a new context.

Published by The Ministry of Defence (MOD), United Kingdom in Ethical Principles for AI in Defence, Jun 15, 2022