SURVEYING THE SECTOR
Now let us consider stakeholders. If we look at the stakeholders on the data and AI sides, we see significant differences. Database administrators, maintenance teams, IT administrators, compliance officers, and business users can all be considered stakeholders in the organisation’ s data.
When we turn our attention to AI, though, we see a far broader range of experts and beneficiaries, from data scientists and machine language engineers to risk managers and lawyers, not to mention ethics specialists and line-of-business executives.
It is vital that all these people collaborate under common leadership. AI governance must be consistent, especially as new models and use cases emerge. Critical to the process is a platform that unites IT, data science, and business management through comprehensive visibility into all projects. This platform must be transparent about performance and status so that information is appropriately actionable.
As we have seen, AI governance and data governance are both essential and distinctive and yet one can have great impact upon the other. Data accuracy and security build trust in different ways; the former relates to the trustworthiness of models, and the latter relates to guarantees of privacy.
AI governance assures internal and external parties that models are being deployed, operated, and monitored fairly, accountably, transparently and in a way that allows those with questions to get answers to those questions in a timely manner.
As time goes on, and AI becomes more and more enmeshed in our everyday lives, AI governance will be the glue that holds everything together. Understanding it today, and how it differs from data governance can prevent predictive and generative solutions causing problems tomorrow.
UAE organisations must strive to enhance the AI literacy of all their people and revisit the concept of governance for the good of all future projects. So, reach for the tiller and steer wisely. �
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