An effective AI governance structure needs to take into account organisational culture, size, strategy and AI maturity.
When deciding on an AI governance structure it’s important to involve a diverse range of colleagues from within an organisation. For example:
- Appropriate C-Suite executives should be involved as project sponsors, as they will often have ultimate responsibility for the AI product development lifecycle;
- Legal and regulatory teams are well versed in ensuring compliance;
- Product teams understand how systems operate from a technical perspective; and
- CSR professionals are accustomed to considering the societal implications of business activity.
Involving diverse, multidisciplinary stakeholders in the process of creating a governance structure will help to bring a diversity of perspectives.
It is advisable to leverage existing organisational processes to help implement ethical AI policies. For example, most companies that collect and handle data have established governance systems that ensure best practice and compliance with data protection legislation. It makes sense to build on these when deciding how to best govern AI.
Further reading about the governance model designed for escalation of ethical issues, as also to learn about mobile operators examples, can be found in the AI Ethics Playbook, Chapter 2.
“At Orange, we are convinced that AI ethics is not negociable, it is the foundation of our AI strategy. We are now organizing ourselves with the support of our group Data and AI Ethics council and per country local AI ethics referent to adapt methodologies and tools.” Steve Jarrett, Senior Vice President, Data and AI Orange Innovation