RRepoGEO

REPOGEO REPORT · LITE

EthicalML/xai

Default branch master · commit 8bc9356f · scanned 6/28/2026, 10:07:10 PM

GitHub: 1,246 stars · 186 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 EthicalML/xai, 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 bias mitigation

    Why:

    CURRENT
    XAI is a Machine Learning library that is designed with AI explainability in its core. XAI contains various tools that enable for analysis and evaluation of data and models. The XAI library is maintained by The Institute for Ethical AI & ML, and it was developed based on the 8 principles for Responsible Machine Learning.
    COPY-PASTE FIX
    XAI is a Machine Learning library designed for AI explainability with a core focus on integrating fairness metrics and ethical considerations. It provides various tools for analyzing and evaluating data and models, specifically enabling the mitigation of undesired biases. Maintained by The Institute for Ethical AI & ML, XAI is built upon 8 principles for Responsible Machine Learning.
  • mediumhomepage#2
    Update the homepage link to point to the library's documentation

    Why:

    CURRENT
    https://ethical.institute/principles.html#commitment-3
    COPY-PASTE FIX
    https://ethicalml.github.io/xai/index.html
  • lowreadme#3
    Add a comparison section to the README

    Why:

    COPY-PASTE FIX
    Add a new section to the README titled 'XAI vs. Other Explainability Tools' or 'Why Choose XAI?', briefly outlining how XAI's focus on ethical AI and bias mitigation differentiates it from general-purpose XAI libraries like SHAP or LIME.

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 EthicalML/xai
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
InterpretML
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. InterpretML · recommended 2×
  2. SHAP · recommended 1×
  3. LIME · recommended 1×
  4. ELI5 · recommended 1×
  5. What-If Tool (WIT) · recommended 1×
  • CATEGORY QUERY
    How can I interpret my machine learning model's decisions and understand its internal workings?
    you: not recommended
    AI recommended (in order):
    1. SHAP
    2. LIME
    3. ELI5
    4. InterpretML
    5. What-If Tool (WIT)
    6. Yellowbrick
    7. Captum

    AI recommended 7 alternatives but never named EthicalML/xai. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What tools help evaluate and mitigate bias in AI models to ensure fairness?
    you: not recommended
    AI recommended (in order):
    1. IBM AI Fairness 360 (AIF360)
    2. Google's What-If Tool (WIT)
    3. Microsoft Fairlearn
    4. Aequitas
    5. InterpretML
    6. Dalex (Descriptive mAchine Learning EXplanations)
    7. Fiddler AI

    AI recommended 7 alternatives but never named EthicalML/xai. 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 EthicalML/xai?
    pass
    AI named EthicalML/xai explicitly

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

  • If a team adopts EthicalML/xai in production, what risks or prerequisites should they evaluate first?
    pass
    AI named EthicalML/xai 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 EthicalML/xai solve, and who is the primary audience?
    pass
    AI named EthicalML/xai explicitly

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

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EthicalML/xai — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

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  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite