S

SHAP

Open Source
Explainability and Fairness Updated Feb 15, 2026
Compared with 13 other Explainability and Fairness agents Open source Self-hostable Updated Feb 2026
💰 Open Source 🔄 Updated Feb 2026 🖥️ Self-hostable

💰 Pricing

Open Source

Pricing not publicly listed.

🎯 Use Cases

Explaining black-box model predictions in healthcare diagnostics Debugging bias or unexpected behavior in credit scoring models Validating feature importance in marketing attribution models Providing transparency for regulatory compliance in financial services

⚖️ Pros & Cons

✅ Pros

  • Works with a wide variety of machine learning models
  • Provides both global and local interpretability
  • Backed by theoretical foundations in game theory

❌ Cons

  • Computationally expensive for large datasets or complex models
  • Interpretations can sometimes be counterintuitive for correlated features
  • Requires some expertise to properly interpret results

Overview

SHAP (SHapley Additive exPlanations) is a Python library that provides interpretable explanations for machine learning model predictions by calculating feature contributions using game theory. It supports a wide range of models, including deep learning and tree-based models, and helps users understand how input features influence predictions.

Problem It Solves

It solves the problem of model interpretability by quantifying the impact of each feature on individual predictions, making complex models more transparent and trustworthy.

Target Audience: Developers and teams working with explainability and fairness automation.

Inputs

  • User configuration
  • API credentials (if required)
  • Task parameters

Outputs

  • Automated task results
  • Status reports
  • Generated content or actions

Example Workflow

  1. 1 User configures the agent with required parameters
  2. 2 Agent receives input data or trigger
  3. 3 Agent processes the request using its core logic
  4. 4 Agent interacts with external services if needed
  5. 5 Results are returned to the user

Sample System Prompt


              You are SHAP, an AI assistant. Help the user accomplish their task efficiently.

            

Tools & Technologies

LLM APIs Python

Alternatives

See all Explainability and Fairness alternatives to SHAP →

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FAQs

Is this agent open-source?
Yes
Can this agent be self-hosted?
Yes
What skill level is required?
Intermediate

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SHAP