AI Ecosystems

AI consists of a multitude of technologies that are applied to many different tasks by a variety of stakeholders. This complexity of the aspects of AI, combined with the complexity of ethical issues of AI means that there is no simple way of addressing the ethics of AI as a whole. As a consequence, we need to find ways of thinking about AI that allows for a broader perspective which, at the same time, offers ways of addressing ethical issues.

One way of thinking about AI is to use the metaphor of ecosystems. This metaphor has been widely accepted and informs, for example, the European Commission's position on AI, which talks about an ecosystem of excellence in AI and an ecosystem of trust.

This use of the ecosystem metaphor raises the question of what can be learned from it that can be applied to addressing ethical issues of AI.

There are different types and groups of stakeholders involved in ethical issues of AI:

• Policy stakeholders including national, regional and international policymakers and those involved in implementing policies, such as research funders

• Organisations including developers, deployers and users of AI as well as organisations with special roles, such as standardisation bodies, educational institutions

• Individuals such as technical experts and developers but also users and, importantly, people who don't use AI but may still be affected by it

Ecosystems, in particular innovation ecosystems mirror natural ecosystems in that their boundaries of are often difficult to define. Members of ecosystems co-evolve, they compete but they also collaborate and learn from each other. There are mutual interdependencies between members but the relationships are typically dynamic

In order to intervene in AI ecosystems to promote human flourishing, three requirements should be met:

• the ecosystem in question needs to be clearly delineated, e.g. in terms of geography, technology, but also conceptually, i.e. with regards to a shared understanding of human flourishing

• a successful AI ecosystem will need to develop and maintain a knowledge base covering technical but also conceptual and procedural knowledge

• governance intervention need to be adaptive, flexible and geared towards learning.

These requirements inform the SHERPA recommendations.