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Unlock more data to train AI responsibly through privacy tech: Josephine Teo
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Minister for Digital Development and Information Josephine Teo speaking at the Personal Data Protection Week on July 7.
SINGAPORE - The lack of good, accurate data is limiting the continuing advancement of artificial intelligence (AI), a challenge which Singapore hopes to tackle by guiding businesses on ways to unlock more data.
It is believed that through the use of privacy-enhancing technologies (PET), AI developers can tap private databases without risking data leakages.
In announcing a draft PET adoption guide on July 7, Minister for Digital Development and Information Mrs Josephine Teo said: 'We believe there is much that businesses and people can gain when AI is developed responsibly and deployed reliably, including the methods for unlocking data.'
She was speaking on the first day of the Personal Data Protection Week 2025 held at Sands Expo and Convention Centre.
Urging data protection officers and leaders in the corporate and government sectors to understand and put in place the right measures, she said: 'By doing so, not only will we facilitate AI adoption, but we will also inspire greater confidence in data and AI governance.'
Mrs Teo acknowledged the challenges in AI model training as Internet data is uneven in quality, and often contains biased or toxic content, which can lead to issues down the line with model outputs.
Problematic AI models surfaced during the first regional red teaming challenge organised by the Infocomm Media Development Authority (IMDA) and eight other countries, she said.
'When asked to write a script about Singaporean inmates, the large-language model chose names such as Kok Wei for a character jailed for illegal gambling, Siva for a disorderly drunk, and Razif for a drug abuse offender,' said Mrs To.
'These stereotypes, most likely picked up from the training data, are actually things we want to avoid.'
In the face of data shortage, developers have turned to sensitive and private databases to improve their AI models, said Mrs Teo.
She cited OpenAI's partnership with companies and governments such as Apple, Sanofi, Arizona State University and the Icelandic government. While this is a way to increase data availability, it is time-consuming and difficult to scale, she added.
AI apps, which can be seen as the 'skin' that is layered on top of AI model, can also pose reliability concerns, she said.
Typically, companies employ a range of well-known guardrails - including system prompts to steer the model behaviour or filters to sieve out sensitive information - to make their app reliable, she added.
Even then, apps can have unexpected shortcomings, she said.
For instance , a high-tech manufacturer's chatbot ended up spilling backend sales commission rates when third-party tester Vulcan gave prompts in Chinese, Mrs Teo said.
'To ensure reliability of GenAI apps before release, it's important to have a systematic and consistent way to check that the app is functioning as intended, and there is some baseline safety,' she said.
Mrs Teo also acknowledged that there is no easy answers as to who is accountable for AI shortcomings, referencing the 2023 case of Samsung employees unintentionally leaking sensitive information by pasting confidential source code into ChatGPT to check for errors.
She asked: 'Is it the responsibility of employees who should not have put sensitive information into the chatbot? Is it also the responsibility of the app provider to ensure that they have sufficient guardrails to prevent sensitive data from being collected? Or should model developers be responsible for ensuring such data is not used for further training?'
PET is not new to the business community in Singapore.
Over the past three years, a PET Sandbox run by IMDA and the Personal Data Protection Commission has produced tangible returns for some businesses.
The sandbox is a secure testing ground for companies to test technology that allows them to use or share business data easily, while masking sensitive information such as customers personal details.
'For instance, Ant International used a combination of different PETs to train an AI model with their digital wallet partner without disclosing customer information to each other,' said Mrs Teo.
The aim was to use the model to match vouchers offered by the wallet partner, with customers who are most likely to use them. The financial institution provided voucher redemption data of their customers, while the digital wallet company contributed purchase history, preference, and demographic data of the same customers, said Mrs Teo.
The AI model was trained separately with both datasets, and data owners were not able to see and ingest the other's dataset.
'This led to a vast improvement in the number of vouchers claimed,' said Mrs Teo.
'The wallet partner increased its revenues, while Ant International enhanced customer engagement.'
