RRepoGEO

REPOGEO REPORT · LITE

Trusted-AI/AIX360

Default branch master · commit 1ea7fc1f · scanned 6/21/2026, 4:51:54 PM

GitHub: 1,782 stars · 327 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

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
    Reposition the README's opening paragraph to highlight AIX360's comprehensive toolkit nature

    Why:

    CURRENT
    The AI Explainability 360 toolkit is an open-source library that supports interpretability and explainability of datasets and machine learning models. The AI Explainability 360 Python package includes a comprehensive set of algorithms that cover different dimensions of explanations along with proxy explainability metrics. The AI Explainability 360 toolkit supports tabular, text, images, and time series data.
    COPY-PASTE FIX
    AI Explainability 360 (AIX360) is a comprehensive open-source toolkit from IBM Research designed to provide interpretability and explainability for diverse machine learning models and datasets. Unlike individual explanation algorithms, AIX360 offers a broad suite of methods covering various explanation dimensions (local, global, pre-model, post-hoc) and data types (tabular, text, images, time series), making it a central resource for understanding AI decisions.
  • mediumtopics#2
    Add more specific topics emphasizing 'toolkit' and 'framework' aspects

    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, codait, deep-learning, explainabil, explainable-ai, explainable-ml, ibm-research, ibm-research-ai, machine-learning, trusted-ai, trusted-ml, xai, xai-toolkit, explainable-ai-framework, model-interpretability-toolkit, ibm-ai-research
  • lowreadme#3
    Add a dedicated 'Comparison' section to the README

    Why:

    COPY-PASTE FIX
    Add a new section to the README, for example, '## Why AI Explainability 360? (AIX360 vs. LIME, SHAP, InterpretML)'. In this section, briefly outline how AIX360's comprehensive suite of algorithms and support for various data types and explanation dimensions differentiates it from more specialized tools or other general toolkits.

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
LIME
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. LIME · recommended 2×
  2. SHAP · recommended 2×
  3. ELI5 · recommended 2×
  4. InterpretML · recommended 2×
  5. Yellowbrick · recommended 1×
  • CATEGORY QUERY
    How can I interpret and explain predictions made by my machine learning models?
    you: not recommended
    AI recommended (in order):
    1. LIME
    2. SHAP
    3. ELI5
    4. InterpretML
    5. Yellowbrick
    6. Skater
    7. Dalex

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

    Show full AI answer
  • CATEGORY QUERY
    Toolkit for understanding why my AI models make specific decisions across various data types?
    you: not recommended
    AI recommended (in order):
    1. LIME
    2. SHAP
    3. InterpretML
    4. Captum
    5. ELI5
    6. What-If Tool

    AI recommended 6 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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Trusted-AI/AIX360 — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite