DATA & ANALYTICS
“ Research has identified critical risks within AI algorithms , including racial , gender and socioeconomic biases and age verification issues ”
NINA BRYANT SENIOR MD , TECHNOLOGY FTI CONSULTING model , ensuring that it aligns with the data on which the model was trained .
“ A primary concern is data drift , which refers to changes in the data used to feed the model . This can occur for various reasons , such as a breakdown in data feeds and fluctuations in the behaviour of the entities the data relates to , for example , in customer behaviour caused by events like a pandemic . When data drift happens , it can significantly affect the model ’ s performance . Without proper governance , you might end up with inaccurate and unreliable AI output .”
Another critical aspect of maintaining AI reliability , Webster says , is recognising and addressing different forms of drift , not just data drift . “ It could be technical issues within the model , such as components not functioning as intended . Effective AI governance ensures these issues are detected and resolved promptly .”
Transparency and accountability When it comes to transparency and accountability , Bryant considers it essential for effective data governance to incorporate checks and balances : “ Core to effective data governance is a series of checks and balances , procedures and controls , that assess the risk of potential harm from new AI solutions .
These ensure that as AI is developed key considerations are managed and documented to drive transparency and reduce the risk of unintended bias .”
Tooley stresses the role of a unified data governance model for achieving transparency : “ Increasingly , organisations need to move towards a single platform that can connect , integrate and automate all of the data management capabilities . This approach provides greater visibility , but it also offers synergy and simplicity . It ensures organisations can integrate data from multiple points and apply powerful AI principles , while ensuring the quality , governance and traceability of data is built into the underlying data management models for AI .”
As Webster concludes , ultimately , humans should be accountable for AI . “ While some people talk about giving AI systems legal rights , accountability must rest with those who make decisions about AI use and deployment . It ’ s the responsibility of humans to ensure that AI systems are governed correctly , that biases are addressed , and that ethical considerations are upheld . Having strong data and AI governance practices in place helps uphold accountability by guiding the responsible use of AI .”
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