以人為本的設計
We design Blue Yonder AI systems around human responsibilities and business outcomes. We aim to align automation, oversight, and safeguards with the level of risk to keep accountability clear.
AI Literacy and Responsible Usage
We promote AI literacy by making role-relevant information about our AI Systems available to users where appropriate. We encourage responsible use that considers expected benefits, potential risks, and best practices, while remaining aligned with the context and intended use cases for which our AI systems were designed.
Transparency and Explainability
We aim to provide users with clear and appropriate information about Blue Yonder AI Systems, including when AI is being used. Where appropriate, we provide understandable information about relevant data sources, material factors, and human oversight behind significant AI outputs or actions so users can interpret and govern their use responsibly.
Fair, Unbiased, and Non-Discriminatory
We design and operate AI Systems with the goal of supporting fair and non-discriminatory outcomes. We put in place proportionate measures, including tools, testing, and ongoing monitoring, to identify, assess, and reduce unfair bias in data, models, and processes across the AI lifecycle.
Data Security and Privacy
We design and operate AI Systems with technical and organizational controls intended to protect customer data, consistent with customer agreements and applicable law. We aim to minimize data use to what is needed for the intended purpose and apply access, retention, and privacy protections proportionate to risk.
負責任的人工智慧
Blue Yonder 致力於以合乎道德且安全的方式利用 AI 來改變供應鏈運作。我們秉持隱私權、數據保護和數據資安的最高標準。秉持同理心、誠信與團隊合作的核心價值觀，以下原則闡述了我們的負責任的 AI 實作方針、對法規遵循的承諾（包括歐盟《人工智慧法案》與《一般資料保護規範》），以及在為客戶創造利益時提供 AI 解決方案的基本信念。
- 負責任的人工智慧
原則
人工智慧資安
We build AI Systems with security and resilience in mind, including controls for AI specific threats where relevant. We use risk-based testing and vulnerability management along with detailed reporting and logging to assess and improve these controls over time.
Governance and Accountability
We use governance processes for AI Systems throughout the life cycle, including defined roles, review practices, and escalation paths. We evaluate risk to align the level of controls with the degree of impact.
Applicability
We offer AI Systems where they can deliver meaningful value for the use case and operating context. Before deploying or expanding automation, we assess fitness for purpose, limitations, and potential impacts.
Sustainability
We consider environmental sustainability in how we build and run AI Systems. Where AI-enabled optimization may improve efficiency, we aim to support reduced waste and resource use.
Trustworthy and Reliable
We design and build our AI Systems to perform reliably for their intended use through in house and real-world validation, quality controls, and continual improvement.
