Microsoft’s framework for building AI systems responsibly
Microsoft’s framework for building AI systems responsibly
微软负责任人工智能系统构建框架
Today we are sharing publicly Microsoft’s Responsible AI Standard, a framework to guide how we build AI systems. It is an important step in our journey to develop better, more trustworthy AI. We are releasing our latest Responsible AI Standard to share what we have learned, invite feedback from others, and contribute to the discussion about building better norms and practices around AI. 今天，我们正式公开了微软的《负责任人工智能标准》（Responsible AI Standard），这是一个旨在指导我们如何构建人工智能系统的框架。这是我们迈向开发更好、更值得信赖的人工智能之旅中的重要一步。我们发布最新的《负责任人工智能标准》，旨在分享我们的经验，邀请各界反馈，并为围绕构建更好的人工智能规范和实践的讨论做出贡献。
Guiding product development towards more responsible outcomes AI systems are the product of many different decisions made by those who develop and deploy them. From system purpose to how people interact with AI systems, we need to proactively guide these decisions toward more beneficial and equitable outcomes. That means keeping people and their goals at the center of system design decisions and respecting enduring values like fairness, reliability and safety, privacy and security, inclusiveness, transparency, and accountability. 引导产品开发实现更负责任的成果。人工智能系统是开发和部署它们的人员所做出的许多不同决策的产物。从系统目的到人们如何与人工智能系统交互，我们需要主动引导这些决策，以实现更有益和更公平的结果。这意味着要将人及其目标置于系统设计决策的核心，并尊重公平、可靠与安全、隐私与安全、包容性、透明度和问责制等持久价值。
The Responsible AI Standard sets out our best thinking on how we will build AI systems to uphold these values and earn society’s trust. It provides specific, actionable guidance for our teams that goes beyond the high-level principles that have dominated the AI landscape to date. The Standard details concrete goals or outcomes that teams developing AI systems must strive to secure. These goals help break down a broad principle like ‘accountability’ into its key enablers, such as impact assessments, data governance, and human oversight. Each goal is then composed of a set of requirements, which are steps that teams must take to ensure that AI systems meet the goals throughout the system lifecycle. Finally, the Standard maps available tools and practices to specific requirements so that Microsoft’s teams implementing it have resources to help them succeed. 《负责任人工智能标准》阐述了我们关于如何构建人工智能系统以维护这些价值并赢得社会信任的最佳思考。它为我们的团队提供了具体、可操作的指导，超越了迄今为止主导人工智能领域的高层原则。该标准详细列出了开发人工智能系统的团队必须努力实现的具体目标或成果。这些目标有助于将“问责制”等广泛原则分解为关键要素，例如影响评估、数据治理和人工监督。每个目标由一系列要求组成，这些要求是团队在整个系统生命周期内确保人工智能系统达到目标所必须采取的步骤。最后，该标准将现有的工具和实践映射到具体要求，以便实施该标准的微软团队拥有帮助他们取得成功的资源。
The core components of Microsoft’s Responsible AI Standard The need for this type of practical guidance is growing. AI is becoming more and more a part of our lives, and yet, our laws are lagging behind. They have not caught up with AI’s unique risks or society’s needs. While we see signs that government action on AI is expanding, we also recognize our responsibility to act. We believe that we need to work towards ensuring AI systems are responsible by design. 微软《负责任人工智能标准》的核心组成部分。对这类实用指导的需求正在增长。人工智能正日益成为我们生活的一部分，然而，我们的法律却滞后了。它们尚未跟上人工智能的独特风险或社会需求。虽然我们看到政府在人工智能方面的行动正在扩大，但我们也认识到我们有责任采取行动。我们相信，我们需要努力确保人工智能系统在设计之初就是负责任的。
Refining our policy and learning from our product experiences Over the course of a year, a multidisciplinary group of researchers, engineers, and policy experts crafted the second version of our Responsible AI Standard. It builds on our previous responsible AI efforts, including the first version of the Standard that launched internally in the fall of 2019, as well as the latest research and some important lessons learned from our own product experiences. 完善我们的政策并从产品经验中学习。在过去的一年中，由研究人员、工程师和政策专家组成的多学科小组制定了我们《负责任人工智能标准》的第二个版本。它建立在我们之前负责任人工智能工作的基础上，包括 2019 年秋季在内部推出的第一版标准，以及最新的研究成果和我们从自身产品经验中汲取的一些重要教训。
Fairness in Speech-to-Text Technology The potential of AI systems to exacerbate societal biases and inequities is one of the most widely recognized harms associated with these systems. In March 2020, an academic study revealed that speech-to-text technology across the tech sector produced error rates for members of some Black and African American communities that were nearly double those for white users. We stepped back, considered the study’s findings, and learned that our pre-release testing had not accounted satisfactorily for the rich diversity of speech across people with different backgrounds and from different regions. After the study was published, we engaged an expert sociolinguist to help us better understand this diversity and sought to expand our data collection efforts to narrow the performance gap in our speech-to-text technology. In the process, we found that we needed to grapple with challenging questions about how best to collect data from communities in a way that engages them appropriately and respectfully. We also learned the value of bringing experts into the process early, including to better understand factors that might account for variations in system performance. The Responsible AI Standard records the pattern we followed to improve our speech-to-text technology. As we continue to roll out the Standard across the company, we expect the Fairness Goals and Requirements identified in it will help us get ahead of potential fairness harms. 语音转文字技术中的公平性。人工智能系统加剧社会偏见和不平等的潜力是与这些系统相关的最广为人知的危害之一。2020 年 3 月，一项学术研究显示，整个科技行业的语音转文字技术在某些黑人和非裔美国人群体中的错误率几乎是白人用户的两倍。我们退后一步，审视了研究结果，并认识到我们的发布前测试未能充分考虑到不同背景和地区人们语音的丰富多样性。研究发表后，我们聘请了一位社会语言学专家来帮助我们更好地理解这种多样性，并寻求扩大我们的数据收集工作，以缩小我们语音转文字技术的性能差距。在此过程中，我们发现我们需要解决一些具有挑战性的问题，即如何以适当且尊重的方式从社区收集数据。我们还认识到尽早引入专家的价值，包括更好地理解可能导致系统性能差异的因素。《负责任人工智能标准》记录了我们改进语音转文字技术所遵循的模式。随着我们继续在全公司推广该标准，我们预计其中确定的公平性目标和要求将帮助我们防范潜在的公平性危害。
Appropriate Use Controls for Custom Neural Voice and Facial Recognition Azure AI’s Custom Neural Voice is another innovative Microsoft speech technology that enables the creation of a synthetic voice that sounds nearly identical to the original source. AT&T has brought this technology to life with an award-winning in-store Bugs Bunny experience, and Progressive has brought Flo’s voice to online customer interactions, among uses by many other customers. This technology has exciting potential in education, accessibility, and entertainment, and yet it is also easy to imagine how it could be used to inappropriately impersonate speakers and deceive listeners. Our review of this technology through our Responsible AI program, including the Sensitive Uses review process required by the Responsible AI Standard, led us to adopt a layered control framework: we restricted customer access to the service, ensured acceptable use cases were proactively defined and communicated through a Transparency Note and Code of Conduct, and established technical guardrails to help ensure the active participation of the speaker when creating a synthetic voice. Through these and other controls, we helped protect against misuse, while maintaining beneficial uses of the technology. Building upon what we learned from Custom Neural Voice, we will apply similar controls to our facial recognition services. After a transition period for existing customers, we are limiting access to these services to managed customers and partners, narrowing the use cases to pre-defined acceptable ones, and leveraging technical controls engineered into the services. 自定义神经语音和人脸识别的适当使用控制。Azure AI 的自定义神经语音（Custom Neural Voice）是微软另一项创新的语音技术，它能够创建听起来与原始来源几乎完全相同的合成语音。AT&T 通过屡获殊荣的店内“兔八哥”体验将这项技术带入现实，Progressive 则将 Flo 的声音引入了在线客户互动，此外还有许多其他客户的使用案例。这项技术在教育、无障碍和娱乐领域具有令人兴奋的潜力，但也很容易想象它如何被用于不恰当地冒充说话者并欺骗听众。我们通过“负责任人工智能”计划对该技术进行了审查，包括《负责任人工智能标准》要求的“敏感用途”审查流程，这促使我们采取了分层控制框架：我们限制了客户对该服务的访问，确保通过透明度说明和行为准则主动定义并传达可接受的使用案例，并建立了技术护栏以帮助确保说话者在创建合成语音时的积极参与。通过这些及其他控制措施，我们在防止滥用的同时，保持了该技术的有益用途。基于从自定义神经语音中学到的经验，我们将对人脸识别服务应用类似的控制措施。在为现有客户提供过渡期后，我们将把这些服务的访问权限限制为受管理的客户和合作伙伴，将使用案例缩小到预定义的可接受范围，并利用内置于服务中的技术控制。
Fit for Purpose and Azure Face Capabilities Finally, we recognize that for AI systems to be trustworthy, they need to be appropriate solutions to the problem. 适用性与 Azure 人脸识别功能。最后，我们认识到，人工智能系统要值得信赖，它们必须是解决问题的适当方案。