Explainable AI: Building trust in business decision-making

Integrating RAI and XAI into the Data Science Lifecycle

EDA, Pre-processing and feature engineering and selection

Model development

Model evaluation

Model selection

Business understanding and hypothesis testing

Input data

RAI definition

Data privacy

Data bias

XAI & privacy

Model bias & privacy

Model management

Model accountability

Monitoring

Deployment

Prediction

Bias, XAI & Drifts

Data Privacy

XAI, Prediction bias

Different Components of XAI Framework

A comprehensive XAI ensures that AI systems are transparent, auditable, and fair and helps businesses make better-informed decisions based on AI-generated insights. With this framework, enterprises can confidently understand how AI systems make decisions, the factors behind those decisions, and how to mitigate any associated risks.

Can we explain our data and its features?

Can we explain how agnostic model works?

Feature sensitivity check within exploration Interpretable feature engineering Feature dependency check on target Feature weights calculation on target

Global and local explanations Split and compare quantiles Deep and tree SHAP

Can we explain how specific model works?

Can we explain the risk associated to business?

Gradient-based attribution methods Explanation by simplification GAM plots

Risk monitoring assessment Risk calculation in probabilities/deciles Trade-off between accuracy and interpretability Counterfactuals for decision-making

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