⚡ Quick Summary
Published by Microsoft, this second version of the Responsible AI Standard sets out the general requirements Microsoft applies to its AI systems, dated June 2022. It is the product of a multi-year effort to define product development requirements that operationalise Microsoft's six AI principles, developed with the company's research, policy and engineering teams because "laws and norms had not caught up with AI's unique risks or society's needs".
The standard is organised into six goal areas: Accountability (A1–A5), Transparency (T1–T3), Fairness (F1–F3), Reliability and Safety (RS1–RS3), Privacy and Security (PS1–PS2), and Inclusiveness (I1). Requirements cover impact assessments, review of Restricted and Sensitive Uses, fit for purpose, data governance, human oversight and control, system intelligibility, stakeholder communication, disclosure of AI interaction, quality of service, allocation of resources and opportunities, stereotyping, failure remediation, and ongoing monitoring. Many requirements are tagged to artefacts such as the Impact Assessment template, Transparency Notes, Responsible Release Criteria and Ongoing Evaluation Checkpoints, and several route escalation to the Office of Responsible AI. Each goal adds "Tools and practices" recommendations, including the Guidelines for Human-AI Interaction, Fairlearn, Error Analysis, Interpret ML, CheckList, Datasheets and red teaming.
🧩 What’s Covered
The document moves from a short statement of purpose through six sets of goals to a closing compliance note and copyright statement.
- About this release: explains the policy gap the standard addresses, presents it as a living document that will evolve with new research, technologies, laws and learnings, and invites feedback via a printed link.
- Accountability Goals (A1–A5): impact assessments completed early and reviewed at least annually; identification of Restricted Uses and reporting of Sensitive Uses to the Office of Responsible AI; fit-for-purpose documentation of model inputs, outputs and generalisability; data governance covering collection, annotation, labelling, cleaning, enrichment and aggregation; and human oversight and control, including "automation bias".
- Transparency Goals (T1–T3): intelligibility for decision making, publication of capabilities, intended uses, limitations and evidence of accuracy and performance, and disclosure of AI interaction including labelling of AI-generated image, audio or video outputs.
- Fairness Goals (F1–F3): quality of service across identified demographic groups including marginalised groups; minimisation of disparities in allocation of resources or opportunities in finance, education, employment, healthcare, housing, insurance or social welfare; and minimisation of stereotyping, demeaning or erasing outputs.
- Reliability & Safety Goals (RS1–RS3): definition of reliable and safe behaviour, acceptable error rates and operational factor ranges; failure management with fallbacks, rollback and feature-off plans; and a monitoring inventory, standard operating procedures and ongoing evaluation.
- Privacy, Security and Inclusiveness: three short goals that defer to the Microsoft Privacy Standard, Microsoft Security Policy and Microsoft Accessibility Standards.
- Tools and practices: recommendations attached to specific requirements, including Fairlearn, Error Analysis, Interpret ML, CheckList, Failure Mode and Effects Analysis, Datasheets, Community Jury, Analysis Platform and the Best Practices for Age, Gender Identity, and Ancestry.
💡 Why it matters?
For organisations building their own AI management systems, the standard shows how high-level principles can be decomposed into auditable requirements: named artefacts, review gates, Responsible Release Criteria and recurring evaluation checkpoints. It is useful evidence of a large vendor's operational approach for teams drafting internal policies, designing impact assessment templates or defining release criteria, and for auditors asking what documentation should exist before deployment. Its treatment of Sensitive and Restricted Uses, Transparency Notes and failure remediation gives concrete hooks for assurance work. The document's own framing places it alongside, not inside, external regulation: it states that laws and norms had not caught up with AI's unique risks when the work began.
❓ What’s Missing
The external release has been edited "to remove internal links, references, and examples", so templates, the definitions of Restricted Uses and Sensitive Uses, and the Office of Responsible AI's review procedures are absent. The privacy, security and inclusiveness goals carry no requirements of their own and only point to other Microsoft standards. Nothing maps the requirements to external regimes such as EU or US law, and no measurement methods, thresholds or worked examples are supplied for metrics such as target maximum performance differences. Compliance is also organisation-specific: the standard presumes Microsoft's compliance process, reviewers and privacy controls, so external readers must translate rather than adopt it.
👥 Best For
Best for AI governance and compliance leads who need a worked example of turning principles into requirements, for risk and assurance teams designing impact assessments, release criteria and transparency documentation, and for product and engineering teams that must define evaluations, failure handling and monitoring checkpoints before deploying an AI system.
📄 Source Details
The Microsoft Responsible AI Standard, v2: General Requirements for External Release, published by Microsoft in June 2022, is a 27-page English-language document; the closing notice reads "© 2022 Microsoft Corporation. All rights reserved." It is the second version of the standard and carries no series or reference number. The text was extracted in full from all 27 pages. The only URL printed is a feedback link, https://aka.ms/ResponsibleAIQuestions; a page-1 header also reads "Preview – Microsoft Responsible AI Standard v2 – Introduction".
