Advancing Trustworthy AI to Industrial-Scale Applications: Insights from Ant Group

With the rapid proliferation of AI applications, AI safety has become increasingly critical, and the promotion of trustworthy AI is now a consensus among academia and industry. At the 2022 World AI Conference, Ant Group, together with the China Academy of Information and Communications Technology and Tsinghua University, unveiled the industry’s first industrial‑grade, all‑data‑type AI security testing platform “AntJian”.

Ant Group’s Director of the Large‑Scale Security Machine Intelligence Department, Wang Weiqiang, emphasized that after defining a technical framework for trustworthy AI, the most urgent task is to advance it into the era of industrial applications, building standards and open technologies that benefit the entire ecosystem.

The core framework of trustworthy AI consists of four dimensions: robustness, explainability, privacy protection, and fairness. These are derived from business needs, regulatory requirements, and corporate responsibility.

Ant’s experience shows that robustness must address data noise, drift, and adversarial attacks, especially in security scenarios where even minor drifts can affect high‑value targets. The company has systematized robustness issues across multiple business lines and created a scoring system for robustness testing.

Explainability becomes crucial as AI models evolve from simple to complex deep‑learning architectures. Ant integrates knowledge graphs and model‑knowledge fusion to improve both interpretability and performance, aiming for seamless human‑machine interaction.

Privacy protection and fairness are driven by compliance with laws such as the Personal Information Protection Law, Data Security Law, and GDPR, as well as ethical responsibilities. Ant quantifies fairness issues in search, marketing, security, and credit scenarios, using monitoring dashboards and techniques like delta tuning and prompting to mitigate bias.

To operationalize trustworthy AI, Ant follows a “full‑link and full‑lifecycle” methodology, ensuring data, model, operation, and platform trustworthiness. The approach balances business‑driven metrics with technical innovation, allowing proactive privacy and fairness safeguards before market demand arises.

Ant also contributes to the broader ecosystem by open‑sourcing platforms such as the AI security testing platform “AntJian” and the privacy‑preserving computing framework “YinYu”. These platforms support diverse data types (text, image, table, sequence) and aim to align industry standards for robustness, privacy, fairness, and explainability.

Collaboration with academia is emphasized through joint courses with top universities, bridging theory and practice to prepare the next generation of AI professionals.

In conclusion, trustworthy AI is positioned as a core capability for the digital economy, with Ant Group’s initiatives in open platforms, sustainable development, and ecosystem building illustrating a comprehensive strategy for responsible AI deployment at industrial scale.