{ "datasetId": "ai-product-decision-ownership-map-v1", "schemaVersion": "1.0.0", "createdAt": "2026-07-21", "language": "zh-CN", "license": "https://creativecommons.org/licenses/by/4.0/", "licenseScope": "The seven-area framework, field structure, Chinese descriptions, and blank record created by DragonAI. Third-party source material is excluded and remains under its original terms.", "title": "AI 产品七类决策协作图 v1", "description": "用七类决策记录产品判断、协作输入、专业责任、最小产物、失败追问和复审条件，不生成能力等级或职业结果预测。", "url": "https://course.dragonai.tech/datasets/ai-product-decision-ownership-map-v1.json", "creator": "烛龙智元内容研究组", "disclosureZh": "DragonAI 运营 AI 产品经理课程。本数据集是第一方协作与复盘框架，不是企业统一分工、招聘标准、能力评级、职业认证或结果保证。", "statusValues": [ "verified", "pending", "not_applicable" ], "recordFields": [ "decisionId", "scenario", "productDecision", "collaborationInputs", "specialistAccountability", "observableArtifact", "failureQuestion", "decisionOwner", "evidenceLocation", "status", "reviewAt", "limitations" ], "decisionAreas": [ { "decisionId": "A01", "nameZh": "问题与价值", "productDecisionZh": "用户在什么场景完成什么任务，是否需要 AI，什么暂时不做？", "collaborationInputsZh": [ "用户研究", "业务基线", "非 AI 替代方案", "失败成本" ], "specialistAccountabilityZh": [ "领域专家确认任务与风险", "技术团队确认方案可行性" ], "minimumArtifactZh": "问题卡：用户、任务、当前基线、替代方案、成功与停止条件", "failureQuestionZh": "什么证据会证明 AI 方案不值得继续？", "evidenceIds": [ "google-ml-problem-framing-20260720" ] }, { "decisionId": "A02", "nameZh": "成功与失败", "productDecisionZh": "哪些用户结果、产品指标、系统表现和严重失败决定继续、阻断或停止？", "collaborationInputsZh": [ "用户任务结果", "业务指标", "模型与系统指标", "关键切片" ], "specialistAccountabilityZh": [ "数据与算法角色确认测量有效性", "风险角色确认严重失败边界" ], "minimumArtifactZh": "指标卡：定义、方向、阈值、切片、缺失处理和决定规则", "failureQuestionZh": "平均结果提高时，哪个关键切片仍可能不可接受？", "evidenceIds": [ "google-pair-data-collection-evaluation-20260720", "nist-ai-rmf-core-20260720" ] }, { "decisionId": "A03", "nameZh": "交互与信任", "productDecisionZh": "用户何时应信任、核验、纠错、退出或转人工？", "collaborationInputsZh": [ "用户心智模型", "正常与失败流程", "能力说明", "反馈与接管测试" ], "specialistAccountabilityZh": [ "设计与研究角色验证理解和可用性", "领域角色确认高风险处置" ], "minimumArtifactZh": "行为边界：能力、限制、来源、反馈、确认、接管和退出路径", "failureQuestionZh": "用户过度信任系统时，如何发现并降低伤害？", "evidenceIds": [ "google-pair-mental-models-20260720" ] }, { "decisionId": "A04", "nameZh": "数据与上下文", "productDecisionZh": "输入从哪里来，怎样使用、更新、删除、隔离和处理冲突？", "collaborationInputsZh": [ "来源与用途", "许可或同意", "质量与版本", "保留与删除" ], "specialistAccountabilityZh": [ "数据角色确认质量与血缘", "安全和法务角色确认权限与合规" ], "minimumArtifactZh": "数据规格：来源、用途、权限、版本、质量、冲突、保留和删除规则", "failureQuestionZh": "来源变化、冲突或撤回后，哪些输出需要重新计算或删除？", "evidenceIds": [ "google-pair-data-collection-evaluation-20260720", "nist-ai-rmf-generative-ai-profile-20260720" ] }, { "decisionId": "A05", "nameZh": "系统方案", "productDecisionZh": "为什么选择当前模型、提示词、检索、微调、工具或混合方案？", "collaborationInputsZh": [ "任务与更新频率", "追溯要求", "数据条件", "成本延迟与权限" ], "specialistAccountabilityZh": [ "算法角色确认模型与实验结论", "工程和安全角色确认架构、可靠性与权限" ], "minimumArtifactZh": "方案记录：候选方案、约束、比较、依赖、权限、成本延迟和降级", "failureQuestionZh": "哪个依赖或权限失败会触发降级、人工确认或停止？", "evidenceIds": [ "nist-ai-rmf-core-20260720", "nist-ai-rmf-generative-ai-profile-20260720" ] }, { "decisionId": "A06", "nameZh": "评测与发布", "productDecisionZh": "哪些样例、指标、阈值和严重失败决定试验、灰度、发布、阻断或回滚？", "collaborationInputsZh": [ "真实任务样例", "正常与风险切片", "逐条结果", "发布与回滚门槛" ], "specialistAccountabilityZh": [ "数据与算法角色确认评测运行", "工程、运营和风险角色确认发布准备" ], "minimumArtifactZh": "评测包：样例版本、标签规则、逐条结果、门槛、决定和签署人", "failureQuestionZh": "哪类严重失败不能被总体平均分抵消？", "evidenceIds": [ "google-pair-data-collection-evaluation-20260720", "nist-ai-rmf-core-20260720" ] }, { "decisionId": "A07", "nameZh": "运营与复盘", "productDecisionZh": "上线后怎样发现变化、处理事故、更新证据并重新作决定？", "collaborationInputsZh": [ "采用与任务结果", "错误类型", "成本延迟", "事故与人工接管" ], "specialistAccountabilityZh": [ "工程角色维持监测与恢复", "运营、风险和领域角色执行升级与处置" ], "minimumArtifactZh": "运行卡：监测、告警、责任人、升级、降级、回滚和复审日期", "failureQuestionZh": "哪些变化会让原发布决定失效并要求重新评测？", "evidenceIds": [ "nist-ai-rmf-core-20260720", "nist-ai-rmf-generative-ai-profile-20260720" ] } ], "blankRecord": { "decisionId": "", "scenario": "", "productDecision": "", "collaborationInputs": [], "specialistAccountability": [], "observableArtifact": "", "failureQuestion": "", "decisionOwner": "", "evidenceLocation": "", "status": "pending", "reviewAt": "", "limitations": [] }, "sources": [ { "evidenceId": "google-ml-problem-framing-20260720", "title": "Machine Learning Problem Framing: Overview", "publisher": "Google for Developers", "url": "https://developers.google.com/machine-learning/problem-framing/problem-framing" }, { "evidenceId": "google-pair-mental-models-20260720", "title": "People + AI Guidebook: Mental Models", "publisher": "Google PAIR", "url": "https://pair.withgoogle.com/guidebook-v2/chapter/mental-models/" }, { "evidenceId": "google-pair-data-collection-evaluation-20260720", "title": "People + AI Guidebook: Data Collection + Evaluation", "publisher": "Google PAIR", "url": "https://pair.withgoogle.com/guidebook-v2/chapter/data-collection/" }, { "evidenceId": "nist-ai-rmf-core-20260720", "title": "AI Risk Management Framework Core", "publisher": "National Institute of Standards and Technology", "url": "https://airc.nist.gov/airmf-resources/airmf/5-sec-core/" }, { "evidenceId": "nist-ai-rmf-generative-ai-profile-20260720", "title": "Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile", "publisher": "National Institute of Standards and Technology", "url": "https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.600-1.pdf" } ] }