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KPMG and NLB expose AI misinformation risk

KPMG in Singapore and the National Library Board (NLB) have launched a new initiative, ‘Read to Lead: Building an AI-Ready Mind’, to promote reading as a key professional capability. This year-long programme, announced on 14 July, seeks to equip professionals, managers, executives, and technicians (PMETs) with the skills needed to discern accurate information in an era dominated by AI-generated content.

A recent poll conducted by KPMG and NLB revealed that only 40% of Singaporean PMETs feel confident in distinguishing accurate content from AI-generated misinformation. Despite the prevalence of AI in information dissemination, less than half of the respondents check the original source of statistics before forming opinions. This gap in critical evaluation skills prompted the launch of ‘Read to Lead’, which aims to embed focused reading into the workplace.

Melissa Tam, CEO of NLB, emphasised the importance of reading for critical thinking, stating, “By reading consistently and widely, we learn to ask better questions and become more discerning consumers of AI-generated outputs.” Lee Sze Yeng, Managing Partner at KPMG, added that professionals who read across disciplines will be better equipped to evaluate information, including AI-generated content.

The programme will benefit over 2,000 PMETs and business leaders through expert-led talks, interactive activities, and resources. It kicks off with ‘Knowledge Week’ from 14 to 16 July, featuring a digital library, quizzes, and panel discussions on critical reading and AI risks. Subsequent phases will include a practical toolkit for AI literacy and a ‘Journey Paper’ in 2027 to sustain the conversation on critical reading.

This initiative marks a significant step in preparing Singapore’s workforce for the challenges of the AI era, fostering a culture of informed and critical engagement with information.

This story was selected and published by a human editor, with content adapted from original press material using AI tools. Spot an error? Report it here.

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