luxzia/XAI_techniques.csv

## XAI_techniques.csv

          
            question
            example XAI techniques
            explanations description

            
              how (global model-wide)
              ProfWeight or BRCG or GLRM
              1. Describe general model logic as feature impact or rules or decision-tree 2. if a user is only interested in a high-level view describe what are the top features or rules considered.

            
              why
              LIME or SHAP or ProtoDash
              1. describe what the key features of the inquired instance determine the model's prediction of it. 2. Show similar exampels with the same predicted outcome to justify the model's prediction

            
              why not (a different prediction)
              CEM or ProtoDash (on alternative prediction)
              1. describe what changes are required for the instance to get the alternative prediction and or what features of the instance guarantee the current prediction. 2. show prototypical examples that had the alternate outcomes.

            
              how to change to be that (a different prediction)
              CEM
              higlight feature that if changed (by increasing or decreasing or making absent or making present) could alter the prediction

            
              how to remain to be this (the current prediction)
              CEM
              describe what feature or feature range could guarantee the same prediction

            
              what if
              use feature tools such as sk-learn's PDP and ICE plots
              show how the prediction changes corresponding to the inquired change

            
              performance
              precision or recall or accuracy or F1 or AUC. Quantify uncertainty using IBM Uncertainty Quantification 360.
              1. Provide performance metrics of the model. 2. Show uncertainty information for each prediction. 3. Describe potential strengths and limitations of the model

            
              data
              see examples in IBM FactSheets 360
              document comprehensive information abou the training data such as including the source or provenance or type or size or coverage of population or potential biases

            
              output
              see examples in IBM FactSheets 360
              1. describe the scope of output or system functions. 2. suggest how the output should be used for downstream tasks or user workflow
question	example XAI techniques	explanations description
how (global model-wide)	ProfWeight or BRCG or GLRM	1. Describe general model logic as feature impact or rules or decision-tree 2. if a user is only interested in a high-level view describe what are the top features or rules considered.
why	LIME or SHAP or ProtoDash	1. describe what the key features of the inquired instance determine the model's prediction of it. 2. Show similar exampels with the same predicted outcome to justify the model's prediction
why not (a different prediction)	CEM or ProtoDash (on alternative prediction)	1. describe what changes are required for the instance to get the alternative prediction and or what features of the instance guarantee the current prediction. 2. show prototypical examples that had the alternate outcomes.
how to change to be that (a different prediction)	CEM	higlight feature that if changed (by increasing or decreasing or making absent or making present) could alter the prediction
how to remain to be this (the current prediction)	CEM	describe what feature or feature range could guarantee the same prediction
what if	use feature tools such as sk-learn's PDP and ICE plots	show how the prediction changes corresponding to the inquired change
performance	precision or recall or accuracy or F1 or AUC. Quantify uncertainty using IBM Uncertainty Quantification 360.	1. Provide performance metrics of the model. 2. Show uncertainty information for each prediction. 3. Describe potential strengths and limitations of the model
data	see examples in IBM FactSheets 360	document comprehensive information abou the training data such as including the source or provenance or type or size or coverage of population or potential biases
output	see examples in IBM FactSheets 360	1. describe the scope of output or system functions. 2. suggest how the output should be used for downstream tasks or user workflow