{ "_description": "本文件由 data/ 生成：请改动来源文件，不要改这里。各字段的证据边界见接入指南。", "schemaVersion": "1.0.0", "kind": "entry", "locale": "zh-CN", "id": "explainability", "name": { "zh": "可解释性", "en": "Explainability" }, "page": "https://designvocabulary.com/zh/entries/explainability/", "self": "https://designvocabulary.com/zh/entries/explainability.json", "alternates": { "zh-CN": "https://designvocabulary.com/zh/entries/explainability.json", "en": "https://designvocabulary.com/en/entries/explainability.json" }, "site": { "catalogSchemaVersion": "0.1.0", "entries": 158, "index": "https://designvocabulary.com/zh/entries/index.json", "query": "https://designvocabulary.com/api/query?lang=zh", "guide": "https://designvocabulary.com/zh/agents/", "llms": "https://designvocabulary.com/llms.txt" }, "provenance": { "sourceBacked": [ "entry.definition" ], "editorial": [ "entry.boundary", "entry.signature", "entry.when", "entry.when_not", "entry.tradeoff", "entry.comparison", "entry.rules", "entry.relations", "entry.implementationNote", "entry.principle" ], "scope": "定义参考以上来源；数字界面示例、选择建议、条件关系及配方由本样例编辑，尚未经用户研究验证。", "editorialStatus": "drafted", "specimenData": "fictional", "specimenNote": "示例为原创虚构界面；定义附来源，用法与配方为编辑建议。", "sources": [ { "id": "msr-hax-guidelines-g11", "url": "https://www.microsoft.com/en-us/haxtoolkit/library/", "title": "Microsoft Research · Guidelines for Human-AI Interaction", "checkedAt": "2026-09-15", "archive": { "status": "available", "confirmed": true, "url": "https://web.archive.org/web/20260916054347/https://www.microsoft.com/en-us/haxtoolkit/library/", "capturedAt": "2026-09-16T05:43:47Z", "lookupUrl": "https://web.archive.org/web/20260915*/https://www.microsoft.com/en-us/haxtoolkit/library/" } }, { "id": "nist-four-principles-explainable-ai", "url": "https://www.nist.gov/news-events/news/2020/08/nist-asks-ai-explain-itself", "title": "NIST · NIST Asks A.I. to Explain Itself (Four Principles of Explainable AI)", "checkedAt": "2026-09-15", "archive": { "status": "available", "confirmed": true, "url": "https://web.archive.org/web/20260916055317/https://www.nist.gov/news-events/news/2020/08/nist-asks-ai-explain-itself", "capturedAt": "2026-09-16T05:53:17Z", "lookupUrl": "https://web.archive.org/web/20260915*/https://www.nist.gov/news-events/news/2020/08/nist-asks-ai-explain-itself" } }, { "id": "bansal-2021-explanations-overreliance", "url": "https://arxiv.org/abs/2006.14779", "title": "Bansal et al. · Does the Whole Exceed its Parts? 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The Effect of AI Explanations on Complementary Team Performance (CHI 2021)", "url": "https://arxiv.org/abs/2006.14779", "claim": "该研究发现，为 AI 建议附加解释会提高使用者接受该建议的比例，且这种提升「无论建议本身是否正确」都会发生；也就是说，解释增加了使用者的采信意愿，却没有同步提高人机协作团队的实际准确率，二者出现了脱节。", "checkedAt": "2026-09-15", "archive": { "lookupUrl": "https://web.archive.org/web/20260915*/https://arxiv.org/abs/2006.14779", "status": "available", "checkedAt": "2026-09-16T05:52:08.058Z", "jobId": "spn2-ca81f214340e0a259c2bab721d3c6d122126dea2", "url": "https://web.archive.org/web/20260916055231/https://arxiv.org/abs/2006.14779", "timestamp": "20260916055231" } } ], "relations": [ { "target": "trust-calibration", "kind": "composes_with", "condition": "系统需要同时传达置信信号与决策依据", "reason": "可解释性提供了信任校准所需的原材料之一——讲清依据与局限；但解释本身不能替代对置信信号与真实准确率是否匹配的核查。", "basis": "editorial" }, { "target": "conversational-interface", "kind": "informs", "condition": "使用者可以针对某次结果继续追问「为什么」", "reason": "对话式交互天然提供了追加提问、逐层展开依据的入口，是呈现可核对解释的常见载体之一。", "basis": "editorial" }, { "target": "visibility-of-system-status", "kind": "related", "condition": "系统状态与结果依据都需要被展示出来", "reason": "两者都关注「系统内部发生了什么应当被使用者看到」，但状态可见性关注的是当前在做什么、进展如何，可解释性关注的是已产出结果的依据与局限。", "basis": "editorial" } ], "rules": [ { "when": "系统展示一个结果或建议", "instruction": "必须用使用者能够对照原始输入自行核实的具体依据说明该结果的由来，而不是仅给出一句无法核对的笼统判断。", "strength": "must", "basis": "editorial" }, { "when": "呈现结果依据的同一处", "instruction": "必须同样清楚地说明系统在这类结果上已知会失效或不可靠的情况，不能只讲理由、回避局限。", "strength": "must", "basis": "editorial" }, { "when": "解释内容本身写得连贯、听起来合理", "instruction": "不能把生成的解释当作结果正确性的证明，仍需提供独立于解释之外的核查方式。", "strength": "must", "basis": "editorial" } ], "principle": { "evidenceType": "人机交互设计准则与可解释人工智能原则的编辑解释", "limit": "本词条不测量某种解释呈现方式能把接受率或核查率改变多少，也不假设「解释准确性」（如实反映系统过程）与「结果正确」是同一件事；已读到的研究表明，附加解释会提高使用者的采信意愿，且这种提升不区分建议对错，因此本词条不复述未读过的其他效应量，也不为「该解释多少内容才够」给出通用数值。" }, "demo": null, "version": "0.1.0", "editorialStatus": "drafted", "requirements": [], "demoCapabilities": [], "implementationNote": "本词条尚无可操作示例；插画为编辑示意。应用到产品时仍须验证：展示的依据是否真的对应系统实际使用的输入与规则、局限说明是否覆盖了已知的失效场景、以及界面是否在任何措辞上暗示解释等同于结果正确。", "image": { "src": "/images/explainability.webp", "alt": "虚构的 FIELDNOTES 结果卡片旁展开一个「为什么」面板，面板里三条较短的理由横条与一条颜色明显不同的局限横条并排，背景为暖色调、无法辨认的文字。" }, "searchTerms": [ "explainability", "result rationale", "limits disclosure", "checkable evidence", "not proof of correctness" ] } }