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(165)
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The development of AI systems other than high-risk AI systems in accordance
with the requirements of this Regulation may lead to a larger uptake of ethical and
trustworthy AI in the Union. Providers of AI systems that are not high-risk should be encouraged
to create codes of conduct, including related governance mechanisms, intended to foster the
voluntary application of some or all of the mandatory requirements applicable to high-risk AI
systems, adapted in light of the intended purpose of the systems and the lower risk involved and
taking into account the available technical solutions and industry best practices such as model
and data cards. Providers and, as appropriate, deployers of all AI systems, high-risk or not,
and AI models should also be encouraged to apply on a voluntary basis additional
requirements related, for example, to the elements of the Union’s Ethics Guidelines for
Trustworthy AI, environmental sustainability, AI literacy measures, inclusive and diverse design
and development of AI systems, including attention to vulnerable persons and accessibility to
persons with disability, stakeholders’ participation with the involvement, as appropriate, of
relevant stakeholders such as business and civil society organisations, academia, research
organisations, trade unions and consumer protection organisations in the design and development
of AI systems, and diversity of the development teams, including gender balance. To ensure that
the voluntary codes of conduct are effective, they should be based on clear objectives and key
performance indicators to measure the achievement of those objectives. They should also be
developed in an inclusive way, as appropriate, with the involvement of relevant stakeholders
such as business and civil society organisations, academia, research organisations, trade unions
and consumer protection organisation. The Commission may develop initiatives, including of
a sectoral nature, to facilitate the lowering of technical barriers hindering cross-border
exchange of data for AI development, including on data access infrastructure, semantic and
technical interoperability of different types of data.
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