XAI: Explainable AI

Introduction

Humans and Machines Have different Strength and Weaknesses

The Case for Human Machine Collaboration

DNA

H

- no - Deploy Model ![Deploy Model](/img/ML_Debiasing_AI/02/deploy_model.png)

• Machine Learning process
  1. Obtian Data
  2. Prepare Data
     • Outsouring it to Amazon Mechanical Turk
  3. Train Model
     • Learing Algorithm
     • Loss Fucntion
     • SGD (確率的勾配降下法)
     • Optimization technique

         model.compile(loss='mean_squared_error', optimizer='sgd')
       

     • Taring is also an iteration process.
  4. Evaluate Model
     • Epoch
       1. valication_score
       2. quality_metric
       3. Epoch: Loss improved. Updating best model
  5. Deploy Model
  6. Integrate it with APP
• Inspect and visualize data
• Prepare the data
• Training the model
• Deploy the model

3. Deploying and Testing the Model via DeepLens

• What is DeepLens
• Deploy model to AWS DeepLens
  • we can use the same way to depoly our model to iOS, Mac using CoreML
• Extend AWS DeepLens
• Retrieve attibutes via AWS Rekognition
• Invoke the crime model
• Set up model alerts

1. Explaining the Model
   • What is explainable AI
   • Trust and transparency issues
   • Making algorithms explainable

Conclusion