AI you can trust.

At Infineon, we empower every employee to design, deploy, and use AI responsibly and with trust. Guided by our AI Manifest and our internal corporate rule for Responsible AI, we deliver high performance in alignment with global regulatory standards.

How Responsible AI Is governed at Infineon

Infineon ensures robust Responsible AI governance by defining, operating, and overseeing company‑wide frameworks, processes, and boundary conditions through two key governance bodies:

  • Responsible AI Office (RAI Office): Experts defining, implementing, and monitoring our Responsible AI program.
  • Responsible AI Committee (RAI Committee): Senior leaders overseeing and supervising governance activities, ensuring transparency, and resolving complex cases.

This dual structure ensures compliance with global regulations, promotes trust, and drives accountability across all AI initiatives.

Empowering AI users and developers

AI users

At Infineon, we empower every employee to use AI responsibly to enhance personal productivity and decision‑making. Our people are supported with training on effective, ethical, and secure AI use, clear user guidelines, and expert assistance. We expect AI outputs to be critically reviewed, copyrights respected, and AI‑generated content and AI‑driven decisions to be transparently labeled. Misuse, such as employee surveillance or social scoring, is strictly prohibited. Any AI-related incident must be reported promptly. To ensure accountability, employees and affected third parties can contest AI decisions through our internal integrity line.

AI use case owners

AI use case owners at Infineon are the experts who design, procure, deploy, and own AI Models across the company. They are operationally responsible for ensuring that their AI systems comply with Infineon’s Responsible AI governance framework and applicable regulatory requirements throughout the entire AI lifecycle. This includes managing AI risks and assessments, adhering to Responsible AI processes, ensuring compliance with core Responsible AI principles such as fairness, accuracy, robustness, security, transparency, and data protection, and excluding prohibited AI use cases such as manipulation, exploitation of vulnerabilities, social scoring, and unauthorized biometric surveillance. Innovators are supported with technical guidebooks, hands‑on coaching, and structured frameworks, questionnaires, and tools that enable continuous assessments - before and after deployment - to ensure AI systems remain accurate, fair, and trustworthy.

 

Responsible AI program: Process and principles

Infineon regulatory & Responsible AI governance process

AI risk classification: Classify risks (e.g., prohibited, high risk, low risk) based on regulatory requirements, business- and ethical considerations.

Responsible and trustworthy AI management & final check: Use case owner is asked to mitigate risk (e.g. limiting access to sensitive AI capabilities such as facial recognition and surveillance) and to follow Responsible AI principles throughout the AI lifecycle.

Post-release monitoring: Continuously monitor and reassess relevant requirements and KPIs after deployment including mechanisms to detect and correct model drift, bias or degradation over time.

Our Responsible AI principles (mandatory for all AI systems)

Trustworthy Ai
Trustworthy Ai
Trustworthy Ai

Data quality, fairness, and bias mitigation

  • Ensuring data quality means applying governance and controls, so the data used to develop, validate, and operate AI systems is accurate, relevant, representative, consistent, timely, and correctly labeled for its intended use.
  • Fairness focuses on equitable performance and treatment across individuals and groups.
  • Bias mitigation includes identifying, measuring, and reducing sources of disparities in data and models across the entire AI lifecycle

Accuracy and performance

  • Accuracy is the degree to which the system produces correct outputs (i.e. it matches the true labels or values for a task) measured by appropriate performance metrics.

Robustness, safety, and security

  • Robustness is the ability to maintain performance under noise, shifts, or perturbations.
  • Safety means prevention and control of hazards to people, property, or the environment across the system’s lifecycle.
  • Cyber Security is the protection against adversarial misuse, data/model theft, and system compromise.

Transparency, explainability, and interpretability         

  • Transparency means clarity about the system’s purpose, data use, limitations, and impacts.
  • Explainability is the ability to explain how inputs influence outputs at global or local levels.
  • Interpretability is the ability to understand internal workings of an AI model and how an AI system arrives at its decisions or predictions.

Privacy and data protection

  • Protecting personal and sensitive data through lawful basis, data minimization, purpose limitation, security, user rights enablement, and privacy-by-design.

Documentation and accountability

  •  End-to-end traceability of decisions, data, models, code, and processes; clear roles and responsibilities; auditability and governance.

Human oversight and AI literacy

  • Designing systems that augment rather than replace human judgment; ensuring users and operators understand the system sufficiently to use it safely and effectively.

Sustainable and ethical AI

  • Minimizing environmental impact and aligning with broader ethical principles (e.g., non-maleficence, justice, beneficence, autonomy).

Contact information

Ready to learn more about Responsible AI?

Contact us at responsibleai@infineon.com.

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