人間を中心に置いた設計
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.
責任あるAI
Blue Yonderは、人工知能（AI）を倫理的かつ安全に活用してサプライチェーンの運用を変革することに注力しています。私たちは、最高水準のプライバシー、データ保護、およびデータセキュリティを掲げています。以下の原則は、共感、誠実さ、チームワークという当社のコアバリューに基づき、責任あるAIの運用、規制コンプライアンスへのコミットメント（欧州連合の人工知能法および一般データ保護規則を含む）、およびお客様の利益のためにAIソリューションを提供する際の当社の基本的な信念の概要を示したものです。
- 責任あるAI
原則
AIセキュリティ
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.