XAI Implementation on Trained ML Models
Budget: $10 – $30 USD
**Project Overview:**
I have 6 trained machine learning models 4 trained on tabular data and 2 on image-based data and I need an XAI (Explainable AI) expert to apply a wide variety of interpretation methods to generate both global and local explanations. The goal is to make the model's decisions fully transparent and understandable, even for people with zero technical knowledge.
### Key Tasks:
-Apply multiple XAI techniques(e.g., SHAP, LIME, Partial Dependence Plots, Feature Importance, Grad-CAM, Saliency Maps for images, etc.)
- Generate global explanations (how the model behaves overall)
- Provide local explanations (why the model made specific predictions) with diverse examples
-Ensure explanations are clear, intuitive, and non-technical (suitable for business stakeholders)
-Work directly in the provided Jupyter Notebook(I’ll share the link)
-Understand the data context to deliver meaningful insights
### **Requirements:**
- Strong experience in XAI (Explainable AI) methods**
- Proficiency in Python, ML libraries (SHAP, LIME, etc.), and deep learning interpretability tools
- Ability to simplify complex ML concepts for non-technical audiences
- Experience with both tabular and image-based model explanations
- Clear communication & well-documented code
What I Provide:
- 6 trained models(4 tabular, 2 image-based)
- Jupyter Notebook with the setup
- Dataset details for context
Looking for someone who can start soon, deliver high-quality explanations, and suggest the best XAI approaches for each model.
If you have strong XAI expertise and can make AI decisions crystal clear, I’d love to hear your approach!
Please include:
- Your experience with XAI (especially SHAP/LIME for tabular & Grad-CAM/Saliency for images)
- Examples of past XAI projects (if available)
- How you’d explain complex ML outputs to non-experts
I have 6 trained machine learning models 4 trained on tabular data and 2 on image-based data and I need an XAI (Explainable AI) expert to apply a wide variety of interpretation methods to generate both global and local explanations. The goal is to make the model's decisions fully transparent and understandable, even for people with zero technical knowledge.
### Key Tasks:
-Apply multiple XAI techniques(e.g., SHAP, LIME, Partial Dependence Plots, Feature Importance, Grad-CAM, Saliency Maps for images, etc.)
- Generate global explanations (how the model behaves overall)
- Provide local explanations (why the model made specific predictions) with diverse examples
-Ensure explanations are clear, intuitive, and non-technical (suitable for business stakeholders)
-Work directly in the provided Jupyter Notebook(I’ll share the link)
-Understand the data context to deliver meaningful insights
### **Requirements:**
- Strong experience in XAI (Explainable AI) methods**
- Proficiency in Python, ML libraries (SHAP, LIME, etc.), and deep learning interpretability tools
- Ability to simplify complex ML concepts for non-technical audiences
- Experience with both tabular and image-based model explanations
- Clear communication & well-documented code
What I Provide:
- 6 trained models(4 tabular, 2 image-based)
- Jupyter Notebook with the setup
- Dataset details for context
Looking for someone who can start soon, deliver high-quality explanations, and suggest the best XAI approaches for each model.
If you have strong XAI expertise and can make AI decisions crystal clear, I’d love to hear your approach!
Please include:
- Your experience with XAI (especially SHAP/LIME for tabular & Grad-CAM/Saliency for images)
- Examples of past XAI projects (if available)
- How you’d explain complex ML outputs to non-experts