ai ethics and governance have become a noisy space.
At last count, the ai/en/dashboards/overview/policy” rel=”noopener ugc nofollow” target=”_blank”>OECD tracker It has more than 1,800 national level documents on initiatives, policies, frameworks and strategies from September 2024 (and it seems there are consultants and influencers weighing in on each of them).
However, as Middle City (2021) puts it succinctly in a way that only academic euphemism can, Principles alone cannot ensure ethical ai.
Despite an abundance of high-level guidance, a notable gap remains between policy and real-world implementation. But why is this and how should data science and ai leaders think about it?
In this series, my goal is to promote the maturity of ai ethics and practical governance within organizations by breaking this gap into three components and leverage research and real-world experience to propose strategies and frameworks that have worked in the past. implementation of ai ethics and governance capabilities. to scale.
The first gap I cover is interpretation gapwhich arises from the challenge of applying principles expressed in vague language such as 'humancentricity' and…
