RRepoGEO

REPOGEO REPORT · LITE

Trusted-AI/AIX360

Default branch master · commit 1ea7fc1f · scanned 5/11/2026, 12:07:11 PM

GitHub: 1,775 stars · 327 forks

AI VISIBILITY SCORE
40 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
2 pass · 0 warn · 0 fail
Objective metadata checks
AI knows your name
3 / 3
Direct prompts that named your repo
HOW TO READ THIS REPORT

Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface Trusted-AI/AIX360, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.

Action plan — copy-paste fixes

3 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highreadme#1
    Strengthen README opening to highlight comprehensiveness

    Why:

    CURRENT
    The AI Explainability 360 toolkit is an open-source library that supports interpretability and explainability of datasets and machine learning models.
    COPY-PASTE FIX
    The AI Explainability 360 (AIX360) toolkit is a comprehensive open-source Python library offering a diverse collection of state-of-the-art algorithms for interpretability and explainability of datasets and machine learning models. Unlike tools focused on specific methods, AIX360 provides a 360-degree view across tabular, text, image, and time series data.
  • mediumcomparison#2
    Add a 'Why AIX360?' or 'Comparison' section to README

    Why:

    COPY-PASTE FIX
    Add a new section to the README, for example, under a heading like 'Why Choose AIX360?' or 'AIX360 vs. Other XAI Tools'. This section should briefly explain how AIX360's comprehensive, multi-algorithm approach and support for diverse data types (tabular, text, images, time series) differentiates it from tools that focus on individual algorithms (e.g., SHAP, LIME) or specific use cases.
  • lowtopics#3
    Refine and expand repository topics

    Why:

    CURRENT
    artificial-intelligence, codait, deep-learning, explainabil, explainable-ai, explainable-ml, ibm-research, ibm-research-ai, machine-learning, trusted-ai, trusted-ml, xai
    COPY-PASTE FIX
    artificial-intelligence, deep-learning, explainability, explainable-ai, explainable-ml, interpretability, machine-learning, trusted-ai, trusted-ml, xai, model-explanation, ai-ethics

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?

Same questions for every model — switch tabs to compare answers and rankings.

Recall
0 / 2
0% of queries surface Trusted-AI/AIX360
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
SHAP
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. SHAP · recommended 2×
  2. LIME · recommended 2×
  3. ELI5 · recommended 2×
  4. InterpretML · recommended 2×
  5. What-If Tool · recommended 1×
  • CATEGORY QUERY
    How can I interpret machine learning model predictions and understand their underlying reasoning?
    you: not recommended
    AI recommended (in order):
    1. SHAP
    2. LIME
    3. ELI5
    4. InterpretML
    5. What-If Tool
    6. Yellowbrick
    7. Skater

    AI recommended 7 alternatives but never named Trusted-AI/AIX360. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What open-source Python libraries help explain deep learning models across various data types?
    you: not recommended
    AI recommended (in order):
    1. LIME
    2. SHAP
    3. Captum
    4. ELI5
    5. InterpretML
    6. DeepLIFT
    7. Alibi Explain

    AI recommended 7 alternatives but never named Trusted-AI/AIX360. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    pass

  • README presence
    pass

Self-mention check

Does AI even know your repo exists when asked about it directly?

  • Compared to common alternatives in this category, what is the core differentiator of Trusted-AI/AIX360?
    pass
    AI named Trusted-AI/AIX360 explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • If a team adopts Trusted-AI/AIX360 in production, what risks or prerequisites should they evaluate first?
    pass
    AI named Trusted-AI/AIX360 explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • In one sentence, what problem does the repo Trusted-AI/AIX360 solve, and who is the primary audience?
    pass
    AI named Trusted-AI/AIX360 explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

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MARKDOWN (README)
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  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite