香港可信任人工智能產業聯盟AI people can understand and use
Hong Kong · Industry collaboration
Build AI progresson trust.
We connect industry, technology, policy and public-interest perspectives to make AI responsible, understandable, secure and useful—so governance becomes infrastructure for responsible innovation.
Responsible public services
Support learning and educators
Reflect local language and heritage
Alliance goals
Make Hong Kong AItrustworthy.
Treat safety, clear responsibility and public confidence as infrastructure for innovation, market access and industry growth.
Make AI developed and used in Hong Kong more trustworthy through disclosure, human oversight and reviewable evidence.
Advocate clear, evidence-based governance that protects people while enabling responsible innovation and industry growth.
Develop practical trust models for public use, government, education and Hong Kong culture, with room for new areas and partners.
Trust model directions
Built for realHong Kong needs.
Initial areas are public use, government, education and Hong Kong culture. We welcome AI model developers and vendors to help shape these and future areas. Work is in development; these are not certified or approved standards.
Public-use AI model
Explore AI that is understandable, accessible and useful in everyday life, with clear limits and a route to human help.
Government-use AI model
Explore responsible AI for public services, with privacy protection, explainable decisions and clear human oversight.
Education AI model
Explore age-appropriate learning support that helps educators and students while keeping teachers in control.
Hong Kong culture AI model
Explore AI that understands Cantonese, Traditional Chinese and local cultural context, with care for sources and creators.
Development path
From dialogueto practical work.
We are inviting expertise across sectors to shape useful AI guidance and applications for Hong Kong. Follow the work as it develops—and bring your perspective.
Alliance identity established
The public identity and initial programme direction are defined.
ConfirmedProgramme formation
Contribution routes and governance practices are being shaped.
In formationFormalise participation
Publish approved participation and working-group arrangements.
ProposedRelease reviewed outputs
Publish dated, attributable practice notes and updates.
ProposedTrust Lab
Turn principles intoreview questions.
Choose a principle to see the questions it brings into an AI project. This learning aid is not a certification or assurance score.
PRINCIPLE / 01
Who owns the decision?
Clear responsibility begins with a named decision owner, a path to raise concerns and a reviewable record.
- Is a decision owner named?
- Which duties remain with the deploying organisation?
- Is evidence retained for review?
News & media
Updates &governance reading.
Alliance updates are separated from independently published commentary. External articles are presented for reference and do not by themselves constitute an Alliance position.
Initial programme framework takes shape
The framework centres on guidance, dialogue, capability and international connection.
Alliance website launches in three languages
Public information is available in English, Traditional Chinese and Simplified Chinese.
Related reading
Independent perspectives on trusted AI
Selected published commentary by Leonard Chan and collaborators. Links open the original publisher pages.
Hong Kong as a global hub for AI governance standards
Jiang Miaomiao & Leonard ChanA standards-hub proposal linking ethics, commercial trust, data governance, human–AI interaction and responsible innovation.
Governing AI disorder and protecting the public interest
Leonard ChanClear responsibility, prohibited boundaries, privacy protection and transparent labelling as foundations for public trust.
Building an AI governance system: Hong Kong has much to contribute
Leonard ChanArgues that safety is infrastructure for innovation and calls for layered governance from models to agents, sectors and rights.
Hong Kong can become a global AI governance testbed
Leonard ChanA human-centred case for the right to know, governance sandboxes and Hong Kong as a bridge between Mainland and international practice.
Participate
Contribute experience.Build common ground.
Industry bodies, enterprises, AI model developers and vendors, professional services, universities, researchers and public-interest organisations are welcome to explore collaboration.
Enquire about joining
See the contact details and send an enquiry about participation or model collaboration.