REPOGEO REPORT · LITE

jphall663/awesome-machine-learning-interpretability

Default branch master · commit a1b0337c · scanned 6/23/2026, 8:38:39 AM

GitHub: 4,045 stars · 629 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 jphall663/awesome-machine-learning-interpretability, 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

Rephrase README opening to emphasize its identity as a curated list

Why:

CURRENT

# Awesome Machine Learning Interpretability
This repository has been reorganized into the HallResearch.ai Library.

COPY-PASTE FIX

# Awesome Machine Learning Interpretability
This repository is a comprehensive, curated list of awesome resources for machine learning interpretability, responsible AI, and related topics. It has since been reorganized into the HallResearch.ai Library.

mediumhomepage#2
Add the homepage URL
Why:
COPY-PASTE FIX
```
https://hallresearch.ai/library
```

lowabout#3

Refine the 'About' description for clarity and specificity

Why:

CURRENT

A curated list of awesome responsible machine learning resources.

COPY-PASTE FIX

A curated list of awesome resources for machine learning interpretability, responsible AI, and related topics.

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 jphall663/awesome-machine-learning-interpretability

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×
What-If Tool · recommended 1×
AI Explainability 360 (AIX360) Toolkit · recommended 1×
InterpretML · recommended 1×

CATEGORY QUERY
What are good resources for learning about explainable AI and model interpretability?
you: not recommended
AI recommended (in order):
1. SHAP
2. LIME
3. What-If Tool
4. AI Explainability 360 (AIX360) Toolkit
5. InterpretML
AI recommended 5 alternatives but never named jphall663/awesome-machine-learning-interpretability. This is the gap to close.
Show full AI answer
CATEGORY QUERY
Where can I find tools and guidance for building responsible and fair machine learning systems?
you: not recommended
AI recommended (in order):
1. TensorFlow Responsible AI Toolkit
2. Microsoft Responsible AI Toolbox
3. IBM AI Fairness 360 (AIF360)
4. Aequitas
5. LIME (Local Interpretable Model-agnostic Explanations)
6. SHAP (SHapley Additive exPlanations)
7. Fiddler AI
AI recommended 7 alternatives but never named jphall663/awesome-machine-learning-interpretability. 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 jphall663/awesome-machine-learning-interpretability?
pass
AI did not name jphall663/awesome-machine-learning-interpretability — 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 jphall663/awesome-machine-learning-interpretability in production, what risks or prerequisites should they evaluate first?
pass
AI named jphall663/awesome-machine-learning-interpretability 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 jphall663/awesome-machine-learning-interpretability solve, and who is the primary audience?
pass
AI did not name jphall663/awesome-machine-learning-interpretability — 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

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MARKDOWN (README)

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HTML

<a href="https://repogeo.com/en/r/jphall663/awesome-machine-learning-interpretability"><img src="https://repogeo.com/badge/jphall663/awesome-machine-learning-interpretability.svg" alt="RepoGEO" /></a>

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jphall663/awesome-machine-learning-interpretability — 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