Data is fundamental to Artificial Intelligence (AI). There is a direct relationship between the quality (and quantity) of what’s fed into a machine-learning application and the accuracy and relevancy of its output.
While data governance has traditionally been viewed in terms of complying with regulations that stipulate how data must be collected, stored, and processed, AI has introduced new challenges and risks that need to be managed. It’s no longer enough to obtain vast amounts of data. One needs to consider its characteristics: Where is it coming from? What does it actually represent? Will it support democratic principles, inclusiveness, and the fair distribution of benefits? Is there anything that you need to account for before feeding this material into your algorithm?
This first event of the Datasphere Initiative, D4D Network and The Global Index on Responsible AI, “AI and data webinar series” discussed the questions that need to be asked to ensure good data governance for AI, and identify challenges in AI development and deployment across regions and sectors.
