How to Implement AI Ethics
In a nutshell
I am creating a framework to help organizations develop AI more responsibly. To do so, I am building on research from responsible innovation, AI ethics, and philosophy, as well as feedback from practitioners and other experts in AI ethics.
I recommend that organizations focus on three pillars of activity around responsible AI:
Knowledge - What should you do to understand how your AI may impact people, society, and the environment?
Workflow - How should you integrate responsible AI components in your workflows and incentives?
Oversight - What should you do to keep yourself accountable?
More details on this framework will be publicly available soon.
Why I work on this project
Figuring out how to implement AI ethics is a pressing need. While there are many documents describing what it means for an AI system to be responsible or ethical, there are very few guidelines on how to implement them (as argued, e.g., by Ayling and Chapman).
Moreover, in practice, most organizations that develop AI do not implement AI ethics. For example, a 2022 IBM survey found that 74% of organizations do nothing to reduce unintended AI bias, and 60% do not even have ethical AI policies.