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microsoft/responsible-ai-toolbox

默认分支 main · commit 94379f64 · 扫描时间 2026/5/10 15:16:17

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AI 可见性总分
33 /100
亟需修复
品类召回
0 / 2
在所有问题中均未被推荐
规则结果
通过 2 · 警告 0 · 失败 0
客观元数据检查
AI 认识你的名字
2 / 3
直接询问时,AI 是否点名你的仓库
如何阅读这份报告

行动计划告诉你下一步要做什么——按影响力排序、可直接复制粘贴的修改。品类可见性是真正的 GEO 测试:当用户向 AI 提一个不带品牌、本应让 microsoft/responsible-ai-toolbox 浮出水面的问题时,AI 是真的推荐了你,还是推荐了你的竞品?客观检查验证 AI 引擎最先权衡的那些元数据信号。自指检查判断 AI 是否还认识你的名字。

行动计划 — 可复制粘贴的修复

3 条由 gemini-2.5-flash 生成、按优先级排序的修改。修完后请把对应条目标记为完成。

整体方向
  • highreadme#1
    Reposition the README's opening statement to emphasize comprehensive Responsible AI assessment

    原因:

    当前
    # Responsible AI Toolbox
    Responsible AI is an approach to assessing, developing, and deploying AI systems in a safe, trustworthy, and ethical manner, and take responsible decisions and actions.
    
    Responsible AI Toolbox is a suite of tools providing a collection of model and data exploration and assessment user interfaces and libraries that enable a better understanding of AI systems.
    复制粘贴的修复
    # Responsible AI Toolbox: A Unified Platform for Holistic AI Assessment and Debugging
    
    The Responsible AI Toolbox is a comprehensive suite of tools designed to empower developers and stakeholders to assess, debug, and monitor AI systems responsibly. Unlike single-purpose tools, our platform provides a holistic view of model behavior, integrating capabilities for fairness, interpretability, error analysis, and causal decision-making into a single pane of glass.
  • mediumtopics#2
    Add topics that highlight the toolbox's comprehensive platform nature

    原因:

    当前
    data-analysis, data-science, data-visualization, error-analysis, explainability, explainable-ai, explainable-ml, fairness, fairness-ai, fairness-ml, interpretability, jupyter, machine-learning, machinelearning, ml, responsible-ai, ui, visualization, widget, widgets
    复制粘贴的修复
    responsible-ai-platform, ai-governance, ai-observability, ml-ops-tools, ai-debugging-tools, responsible-ai, data-analysis, data-science, data-visualization, error-analysis, explainability, explainable-ai, explainable-ml, fairness, fairness-ai, fairness-ml, interpretability, jupyter, machine-learning, machinelearning, ml, ui, visualization, widget, widgets
  • lowreadme#3
    Add an explicit statement about the toolbox's unique value proposition compared to individual tools

    原因:

    复制粘贴的修复
    Unlike many individual tools that focus on a single aspect of Responsible AI, the Responsible AI Toolbox integrates multiple mature capabilities—including interpretability (powered by InterpretML), error analysis, and fairness—into a unified dashboard for holistic model assessment and debugging.

本次扫描解析到的品类 GEO 通道:google/gemini-2.5-flash, deepseek/deepseek-v4-flash

品类可见性 — 真正的 GEO 测试

向 google/gemini-2.5-flash 提出的不带品牌问题。AI 推荐了你,还是推荐了别人?

各模型使用同一组问题 — 切换标签对比回答与排名。

召回
0 / 2
0% 的问题里出现了 microsoft/responsible-ai-toolbox
平均排名
越小越好。#1 表示首位推荐。
声量占比
0%
在所有被点名的工具中,你占了多少?
头号对手
shap/shap
在 2 个问题中被推荐 2 次
竞品排行
  1. shap/shap · 被推荐 2 次
  2. marcotcr/lime · 被推荐 2 次
  3. Trusted-AI/AIX360 · 被推荐 1 次
  4. interpretml/interpret · 被推荐 1 次
  5. PAIR-code/what-if-tool · 被推荐 1 次
  • 品类问题
    How can I assess and debug machine learning models for fairness and interpretability?
    你:未被推荐
    AI 推荐顺序:
    1. IBM AI Explainability 360 (AIX360) (Trusted-AI/AIX360)
    2. Microsoft InterpretML (interpretml/interpret)
    3. Google What-If Tool (WIT) (PAIR-code/what-if-tool)
    4. Google TCAV (Testing with Concept Activation Vectors) (tensorflow/tcav)
    5. Fairlearn (fairlearn/fairlearn)
    6. SHAP (SHapley Additive exPlanations) (shap/shap)
    7. LIME (Local Interpretable Model-agnostic Explanations) (marcotcr/lime)

    AI 推荐了 7 个替代方案,却始终没点名 microsoft/responsible-ai-toolbox。这就是要补上的差距。

    查看 AI 完整回答
  • 品类问题
    What tools help visualize AI system behavior and identify errors for responsible development?
    你:未被推荐
    AI 推荐顺序:
    1. TensorBoard (tensorflow/tensorboard)
    2. Weights & Biases (W&B) (wandb/wandb)
    3. MLflow (mlflow/mlflow)
    4. SHAP (SHapley Additive exPlanations) (shap/shap)
    5. LIME (Local Interpretable Model-agnostic Explanations) (marcotcr/lime)
    6. DeepView.ai
    7. Microsoft InterpretML (microsoft/interpret)

    AI 推荐了 7 个替代方案,却始终没点名 microsoft/responsible-ai-toolbox。这就是要补上的差距。

    查看 AI 完整回答

客观检查

针对 AI 引擎最看重的元数据信号的规则审计。

  • Metadata completeness
    pass

  • README presence
    pass

自指检查

当被直接问到你时,AI 是否还知道你的仓库存在?

  • Compared to common alternatives in this category, what is the core differentiator of microsoft/responsible-ai-toolbox?
    pass
    AI 未点名 microsoft/responsible-ai-toolbox —— 很可能在说另一个项目

    AI 的回答可能信誓旦旦却是错的。请按事实核对:技术栈、目标人群、差异化点是不是和你实际的对得上?

  • If a team adopts microsoft/responsible-ai-toolbox in production, what risks or prerequisites should they evaluate first?
    pass
    AI 明确点名了 microsoft/responsible-ai-toolbox

    AI 的回答可能信誓旦旦却是错的。请按事实核对:技术栈、目标人群、差异化点是不是和你实际的对得上?

  • In one sentence, what problem does the repo microsoft/responsible-ai-toolbox solve, and who is the primary audience?
    pass
    AI 明确点名了 microsoft/responsible-ai-toolbox

    AI 的回答可能信誓旦旦却是错的。请按事实核对:技术栈、目标人群、差异化点是不是和你实际的对得上?

嵌入你的 GEO 徽章

把这个徽章贴进 microsoft/responsible-ai-toolbox 的 README。每次重新扫描都会自动更新,并跳到最新报告——是「我在乎 AI 可发现性」最简单的公开证明。

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订阅 Pro,解锁深度诊断

microsoft/responsible-ai-toolbox — 轻量扫描仍免费;本卡列出 Pro 相对轻量的深度额度。

  • 深度报告每月 10 次
  • 无品牌品类查询5,轻量 2
  • 优先行动项8,轻量 3