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News2 min read·2026-10-21DisconfirmAI Editorial

ICBC President Liu Jun: AI Accountability Should Follow Benefit and Decision

At the Shanghai Bund Summit, Liu Jun, president of the Industrial and Commercial Bank of China, said commercial banks bear direct financial and legal liability when deploying AI at scale, while central bankers focus on oversight and policy. He described a 'proof of benefit' framework that ties accountability to those who reap returns and make independent decisions. Arthur Yuen Kwok-hang, deputy chief executive of the Hong Kong Monetary Authority, added that when one application is split across six to eight agents and each party handles only a part, 'nobody is responsible for the whole thing.'

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Original publication time 2026-10-21 07:00 GMT+8

Liu Jun, president of the Industrial and Commercial Bank of China—the world's largest lender by assets—laid out a specific judgment about AI deployment responsibility at a panel discussion during the Shanghai Bund Summit: commercial banks and central banks sit in asymmetric positions when facing AI risk. Central bankers focus on oversight and policy, while commercial lenders that deploy AI at scale bear direct financial and legal liability. Liu's own words: 'If ICBC deploys AI tools towards different business lines, and all the benefits contribute to the IT balance sheet and income statements, it means I take the responsibility of the AI deployment. If something bad happens, I'm going to have to pay the victim and give the final answer to the market.'

At the same panel, as banks begin adopting agentic AI—systems that can plan and carry out multi-step tasks—Arthur Yuen Kwok-hang, deputy chief executive of the Hong Kong Monetary Authority, echoed Liu's concern about accountability and offered a concrete observation: institutions typically split one application across six to eight agents, but if each agent and the human supervisor are responsible for only part of the task, 'nobody is responsible for the whole thing.' Liu also noted that ICBC has deployed hundreds of AI models and given them more autonomy in data-heavy work, while keeping human teams in client-facing positions.

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To address this fragmentation, Liu described a framework ICBC introduced called 'proof of benefit'—an approach that directly ties accountability to those who reap the financial returns and make independent decisions within the system. The logic is that whoever benefits bears responsibility, rather than diffusing liability vaguely across developers, deployers, and users. Yuen revealed that Hong Kong's de facto central bank had unveiled an updated AI governance framework the previous week, focused on accountability, resilience, and trust, and would publish a discussion paper on supervising agentic AI, exploring accountability tied to intent, a registry of material AI agents, and ways to intervene. Yuen said the Hong Kong government aims to foster an environment in which financial institutions can safely 'fail fast.' 'We, as supervisors, become more facilitative rather than regulatory,' he said.

It is worth noting that the specific operational details of the 'proof of benefit' framework—how 'benefit' is defined, how 'independent decision' is determined, and how decision chains are traced when agents execute autonomously—were not elaborated in the reporting. The HKMA's governance framework has been published, but the discussion paper on agentic AI supervision remains forthcoming and has not yet formed binding rules. Both Liu and Yuen identified the difficulty of assigning responsibility, but the actual effectiveness of their proposed solutions remains to be verified.

Sources

  • 原始出处scmp.com↗
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