Intelligent Build.tech Issue 22 | Page 42

SURVEYING THE SECTOR
Sid Bhatia, AVP and General Manager, Africa, Middle East, and Türkiye, Dataiku
For example, the US National Institute of Standards and Technology published a range of frameworks, including the AI Risk Management Framework, and the AI Bill of Rights; the UK launched the AI Safety Institute; and Singapore’ s Infocomm Media Development Authority released AI Verify. Even the European Union has an AI Act that empowers the levying of significant penalties for non-compliance.
Increasingly, businesses must be aware of, and navigate external rules as they digitalise their operations. Understanding where data governance ends and where AI governance begins is vital to remaining on the good side of regulators. When implementing data governance, we look first to policies, centralised catalogues and stewardship, and strive to maintain data quality.
With AI, things become more complicated, but we can begin with Digital Dubai’ s ethical guidelines and compose risk-assessment frameworks based on these. We can also review our operational efficiency and produce ways to monitor the value-add of each project. Indeed, the effectiveness of AI governance will hinge on the level of observability of AI systems.
It is in the implementation, where we can see the starkest differences between AI and data governance. AI frameworks should stand apart from cataloguing datasets. AI governance is about enforcing rules around the implementation of solutions to avoid a range of errors during development and deployment.
In digitally competitive economies such as those found in the GCC, a misstep could have severe implications for a private business, both in terms of lost consumer trust and regulatory penalties.
Additionally, Generative AI brings in a host of concerns related to AI and data governance. Large language models, for instance, can lead to non-compliance with privacy and residency regulations, as well as hallucination effects emanating from subpar training data. In both these cases, data governance and AI governance become intertwined.
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