---
title: "Explainable AI: Demystifying Complex Models with Shapley Values - Neeraj Pandey"
tags: PyConTW2023, 2023-organize, 2023-共筆
---

# Explainable AI: Demystifying Complex Models with Shapley Values - Neeraj Pandey

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<iframe src=https://app.sli.do/event/6CSRaeDADdcm74k9ywbkzJ height=450 width=100%></iframe>

> Collaborative writing start from below
> 從這裡開始共筆

[Here is the link to the tutorial and slides](https://github.com/neerajp99/pycon-taiwan)
[Slides](https://github.com/neerajp99/pycon-taiwan/blob/master/Explainable%20AI.pdf)

Explainable AI: Help human understand how AI models make certain prediction. Unveil the black box model
e.g. Doctors need to understand why a model makes a certain prediction to trust it and to effectively communicate the risk to patients

- **The black box dilemma**
- Simple model is easier to interpret (e.g. linear regression model). Although complicated neural networks are powerful, they are too complex to interpret.

XAI methods
- LIME: local interpretation
- SHAP

Much accuracy, less interpretability

## Benefit
- Model stability
- Tolereance for error
- Uniform behavior

- Cause and effect
- Beyond correlation
Understanding causality is important
- Informed Predictions
- User confidence
- AI-Human Synergy
- Openness as Trust builder
- lawful accountability
- Right to explanation
- Accountability
- Who is responsible for AI decision?

## Types of explainable AI methods

Types of XAI methods
- Model specific
- Model agnostic

Model scope
- Global
Understand the **overall** behavior of the model
- Local
explain why the model made a specific prediction for a given data point

Global explainability
- feature importance
Like a permutation feature importance
- partial dependence plots (PDPs)
show how individual features affect predictions across different data points.
- linear or logistic regression with regularization

Local explainability
- LIME
- Conterfactual explanations
- SHAP

Below is the part that speaker updated the talk/tutorial after speech
講者於演講後有更新或勘誤投影片的部份