For governments, AI productivity cannot be defined solely by faster processes, lower costs or more automated tasks. Systems must also produce accurate, reliable and explainable results while protecting personal information, supporting human rights and remaining safe under real-world and adversarial conditions.
Dr. Christopher Parsons argues that public value depends on whether employees can understand, question and challenge AI-supported decisions. An organization could record impressive efficiency gains while experiencing low confidence in the outputs and weakening public trust.
Responsible adoption therefore requires clear principles and governance throughout the AI lifecycle. By treating privacy, transparency and accountability as core measures of success, not secondary considerations, governments can improve services while building the trust required for long-term transformation.
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