REPOGEO REPORT · LITE

wangyongjie-ntu/Awesome-explainable-AI

Default branch master · commit e0d200bb · scanned 6/22/2026, 8:18:17 PM

GitHub: 1,650 stars · 222 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

22 /100

Critical

Category recall

0 / 2

Not recommended in any query

Rule findings

1 pass · 1 warn · 0 fail

Objective metadata checks

AI knows your name

1 / 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 wangyongjie-ntu/Awesome-explainable-AI, 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 README's opening sentence to explicitly state 'Awesome List'

Why:

CURRENT

This repository contains the frontier research on explainable AI(XAI) which is a hot topic recently.

COPY-PASTE FIX

This is an **Awesome List** repository containing a curated collection of frontier research materials and surveys on Explainable AI (XAI).

mediumabout#2
Add a homepage URL to the repository metadata
Why:
COPY-PASTE FIX
```
https://github.com/wangyongjie-ntu/Awesome-explainable-AI
```

lowtopics#3

Expand repository topics to reinforce its 'Awesome List' nature

Why:

CURRENT

counterfactual-explanations, explainable-ai, explanation-system, interpretability, interpretable-ai, recourse, xai, xml

COPY-PASTE FIX

counterfactual-explanations, explainable-ai, explanation-system, interpretability, interpretable-ai, recourse, xai, xml, awesome-list, research-papers, machine-learning-research, ai-surveys

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 wangyongjie-ntu/Awesome-explainable-AI

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

SHAP

Recommended in 1 of 2 queries

COMPETITOR LEADERBOARD

SHAP · recommended 1×
LIME · recommended 1×
ELI5 · recommended 1×
InterpretML · recommended 1×
What-If Tool · recommended 1×

CATEGORY QUERY
How can I make my machine learning model predictions more transparent and understandable?
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 wangyongjie-ntu/Awesome-explainable-AI. This is the gap to close.
Show full AI answer
CATEGORY QUERY
Where can I find comprehensive research materials and surveys on explainable AI?
you: not recommended
AI recommended (in order):
1. arXiv.org
2. Google Scholar
3. ACM Digital Library
4. IEEE Xplore
5. ACM Computing Surveys
6. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)
7. NeurIPS
8. ICML
9. AAAI
10. IJCAI
11. KDD
12. Distill.pub
13. Papers With Code
14. The Alan Turing Institute
15. Stanford
16. MIT
17. Carnegie Mellon
18. Oxford
19. Cambridge
20. TU Munich
AI recommended 20 alternatives but never named wangyongjie-ntu/Awesome-explainable-AI. This is the gap to close.
Show full AI answer

Objective checks

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

Metadata completeness
warn
Suggestion:
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 wangyongjie-ntu/Awesome-explainable-AI?
pass
AI did not name wangyongjie-ntu/Awesome-explainable-AI — likely talking about a different project
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts wangyongjie-ntu/Awesome-explainable-AI in production, what risks or prerequisites should they evaluate first?
pass
AI named wangyongjie-ntu/Awesome-explainable-AI 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 wangyongjie-ntu/Awesome-explainable-AI solve, and who is the primary audience?
pass
AI did not name wangyongjie-ntu/Awesome-explainable-AI — likely talking about a different project
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

Embed your GEO score

Drop this badge into the README of wangyongjie-ntu/Awesome-explainable-AI. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

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MARKDOWN (README)

[![RepoGEO](https://repogeo.com/badge/wangyongjie-ntu/Awesome-explainable-AI.svg)](https://repogeo.com/en/r/wangyongjie-ntu/Awesome-explainable-AI)

HTML

<a href="https://repogeo.com/en/r/wangyongjie-ntu/Awesome-explainable-AI"><img src="https://repogeo.com/badge/wangyongjie-ntu/Awesome-explainable-AI.svg" alt="RepoGEO" /></a>

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wangyongjie-ntu/Awesome-explainable-AI — 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