更重要的是，LLM 的强大多样性也使得很难准确预测它们可能会产生的意外或不可预见的输出。鉴于这些风险和复杂情况，Vertex AI 生成式 AI API 在设计时考虑到了 Google 的 AI 原则。但是，开发者必须了解和测试其模型，以便以负责任的方式安全部署。为帮助开发者，Vertex AI Studio 具有内置内容过滤功能，而生成式 AI API 具有安全属性打分功能，可帮助客户测试 Google 的安全过滤器并定义适合其应用场景和业务的置信度阈值。如需了解详情，请参阅安全过滤器和属性部分。
生成式 API 集成到您的独特应用场景和语境中时，您可能需要考虑其他 Responsible AI 注意事项和限制。我们建议客户采用公平性、可解释性、隐私权和安全推荐实践。
[[["易于理解","easyToUnderstand","thumb-up"],["解决了我的问题","solvedMyProblem","thumb-up"],["其他","otherUp","thumb-up"]],[["很难理解","hardToUnderstand","thumb-down"],["信息或示例代码不正确","incorrectInformationOrSampleCode","thumb-down"],["没有我需要的信息/示例","missingTheInformationSamplesINeed","thumb-down"],["翻译问题","translationIssue","thumb-down"],["其他","otherDown","thumb-down"]],["最后更新时间 (UTC)：2024-11-25。"],[],[],null,["# Responsible AI\n\nLarge language models (LLMs) can translate language, summarize text, generate\ncreative writing, generate code, power chatbots and virtual assistants, and\ncomplement search engines and recommendation systems. At the same time, as an\nearly-stage technology, its evolving capabilities and uses create potential for\nmisapplication, misuse, and unintended or unforeseen consequences. Large\nlanguage models can generate output that you don't expect, including text that's\noffensive, insensitive, or factually incorrect.\n\nWhat's more, the incredible versatility of LLMs is also what makes it difficult\nto predict exactly what kinds of unintended or unforeseen outputs they might\nproduce. Given these risks and complexities, Vertex AI generative AI APIs are designed with\n[Google's AI Principles](https://ai.google/principles/) in mind. However, it is important for developers to understand\nand test their models to deploy safely and responsibly. To aid developers, the\nVertex AI Studio has built-in content filtering, and our generative AI APIs have\nsafety attribute scoring to help customers test Google's safety filters and\ndefine confidence thresholds that are right for their use case and business.\nRefer to the [Safety filters and attributes](#safety_filters_and_attributes)\nsection to learn more.\n\nWhen our generative APIs are integrated into your unique use case and context,\nadditional responsible AI considerations and\n[limitations](#limitations)\nmight need to be considered. We encourage customers to promote fairness,\ninterpretability, privacy and security\n[recommended practices](https://ai.google/responsibilities/responsible-ai-practices/).\n\nSafety filters and attributes\n-----------------------------\n\nTo learn how to use safety filters and attributes for an API,\nsee [Gemini API in Vertex AI](/vertex-ai/generative-ai/docs/multimodal/configure-safety-attributes).\n\nModel limitations\n-----------------\n\n*Limitations you can encounter when using generative AI models include (but\nare not limited to):*\n\n- **Edge cases**: Edge cases refer to unusual, rare, or exceptional situations\n that are not well-represented in the training data. These cases can lead to\n limitations in the performance of the model, such as model overconfidence,\n misinterpretation of context, or inappropriate outputs.\n\n- **Model hallucinations, grounding, and factuality** : Generative AI models\n can lack factuality in real-world knowledge, physical properties, or\n accurate understanding. This limitation can lead to model hallucinations,\n which refer to instances where it can generate outputs that are\n plausible-sounding but factually incorrect, irrelevant, inappropriate, or\n nonsensical. To reduce this chance, you can ground the models to your\n specific data. To learn more about grounding in Vertex AI, see\n [Grounding overview](/vertex-ai/generative-ai/docs/grounding/overview).\n\n- **Data quality and tuning**: The quality, accuracy, and bias of the prompt\n or data input into a model can have a significant impact on its\n performance. If users enter inaccurate or incorrect data or prompts, the\n model can have suboptimal performance or false model outputs.\n\n- **Bias amplification**: Generative AI models can inadvertently amplify\n existing biases in their training data, leading to outputs that can further\n reinforce societal prejudices and unequal treatment of certain groups.\n\n- **Language quality** : While the models yield impressive multilingual\n capabilities on the benchmarks we evaluated against, the majority of our\n benchmarks (including all of fairness evaluations) are in the English\n language. For more information, see the\n [Google Research blog](https://ai.googleblog.com/2022/04/pathways-language-model-palm-scaling-to.html).\n\n - Generative AI models can provide inconsistent service quality to different users. For example, text generation might not be as effective for some dialects or language varieties due to underrepresentation in the training data. Performance can be worse for non-English languages or English language varieties with less representation.\n- **Fairness benchmarks and subgroups** : Google Research's fairness analyses\n of our generative AI models don't provide an exhaustive account of the\n various potential risks. For example, we focus on biases along gender, race,\n ethnicity and religion axes, but perform the analysis only on the English\n language data and model outputs. For more information, see the\n [Google Research blog](https://ai.googleblog.com/2022/04/pathways-language-model-palm-scaling-to.html).\n\n- **Limited domain expertise**: Generative AI models can lack the depth of\n knowledge required to provide accurate and detailed responses on highly\n specialized or technical topics, leading to superficial or incorrect\n information. For specialized, complex use cases, models should be tuned on\n domain-specific data, and there must be meaningful human supervision in\n contexts with the potential to materially impact individual rights.\n\n- **Length and structure of inputs and outputs**: Generative AI models have a\n maximum input and output token limit. If the input or output exceeds this\n limit, our safety classifiers are not applied, which could ultimately lead\n to poor model performance. While our models are designed to handle a wide\n range of text formats, their performance can be affected if the input data\n has an unusual or complex structure.\n\nRecommended practices\n---------------------\n\nTo utilize this technology safely and responsibly, it is also important to\nconsider other risks specific to your use case, users, and business context in\naddition to built-in technical safeguards.\n\nWe recommend taking the following steps:\n\n1. Assess your application's security risks.\n2. Perform safety testing appropriate to your use case.\n3. Configure safety filters if required.\n4. Solicit user feedback and monitor content.\n\nReport abuse\n------------\n\nYou can report suspected abuse of the Service or any generated output that\ncontains inappropriate material or inaccurate information by using the following\nform:\n[Report suspected abuse on Google Cloud](https://support.google.com/code/contact/cloud_platform_report).\n\nAdditional resources\n--------------------\n\n- Learn about [abuse monitoring](/vertex-ai/generative-ai/docs/learn/abuse-monitoring).\n- Learn more about Google's recommendations for [Responsible AI practices](https://ai.google/responsibilities/responsible-ai-practices/?category=general).\n- Read our blog, [A shared agenda for responsible AI progress](https://blog.google/technology/ai/a-shared-agenda-for-responsible-ai-progress/)"]]
