Ethical principles
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Avoidance of unfair bias

Datasets used by AI systems, both for training and operation, may suffer from the inclusion of inadvertent historic bias, incompleteness and bad governance models. The continuation of such biases could lead to unintended (in)direct prejudice and discrimination against certain groups or people, potentially exacerbating prejudice and marginalization. Harm can also result from the intentional exploitation of (consumer) biases, or by engaging in unfair competition, such as the homogenization of prices by means of collusion or a non-transparent market.

Identifiable and discriminatory bias should be removed in the collection phase where possible. The way in which AI systems are developed (e.g. algorithms’ programming) may also suffer from unfair bias. This can be counteracted by putting in place oversight processes to analyze and address the system’s purpose, constraints, requirements and decisions in a clear and transparent manner. Moreover, hiring developers from diverse backgrounds, cultures and disciplines can ensure a diversity of opinions – and should therefore be encouraged.

Accessibility and universal design

Systems should be user-centric and designed in a way that allows all people to use AI products or services, regardless of their age, gender, abilities or characteristics. Accessibility to this technology for minors or persons with disabilities, which are present in all societalgroups, is of particular importance. AI systems should not have a one-size-fits-all approach and should consider Universal Design principles, addressing the widest possible range of users, following relevant accessibility standards. This will enable equitable access and active participation of all people in existing and emerging computer-mediated human activities, and with regard to assistive technologies.

Stakeholder participation

To develop AI systems that are trustworthy, it is advisable to consult stakeholders who may directly or indirectly be affected by the system throughout its life cycle. It is beneficial to solicit regular feedback even after deployment, and set up longer-term mechanisms for stakeholder participation, for example by ensuring workers’ information, consultation and participation throughout the whole process of implementing AI systems at organizations.


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