courses:xai:p1_2025

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courses:xai:p1_2025 [2025/11/24 09:53] – admincourses:xai:p1_2025 [2025/12/01 09:24] (current) – [Programming Assignment I] admin
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 Assignments marked with {{:courses:xai:winner.png?25|}} can be extended as a continution in Programming Assgnment II. Assignments marked with {{:courses:xai:winner.png?25|}} can be extended as a continution in Programming Assgnment II.
 Projects marked with {{:courses:xai:contract.png?25|}} can be (with some additional work and depending on the results) published as scientific papers. Projects marked with {{:courses:xai:contract.png?25|}} can be (with some additional work and depending on the results) published as scientific papers.
 +
 +Select your project: 
 +  * [Monday group]: [[https://ujchmura-my.sharepoint.com/:x:/g/personal/szymon_bobek_uj_edu_pl/IQDwhgwVco6YS53jp8DRACWHAcdSSHvoObQeqkbFPXpTwHc?e=YSBAr2| Choose your project]]
  
  
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-===== TSProto for Colab ===== 
  
-Try to run tutorials of [[https://tsproto.readthedocs.io/en/latest/|TSProto]] in Colab (it will require some code tweeking/requirements adjustment to work with numpy 2.0.0. 
-Once done, create a pull request to include the changes in the current implementation of TSProto 
  
  
 ===== Counterfactual Analysis ===== ===== Counterfactual Analysis =====
 +{{:courses:xai:winner.png?30|}}
  
 Benchmark 3 models on 3 datasets (e.g., Breast Cancer, Adult, Heart Disease), explain the best model using (SHAP, Lime, and feature importance), and get counterfactual explanations using one of these frameworks (ACFX, DICE, CLUE, CFNOW) . Benchmark 3 models on 3 datasets (e.g., Breast Cancer, Adult, Heart Disease), explain the best model using (SHAP, Lime, and feature importance), and get counterfactual explanations using one of these frameworks (ACFX, DICE, CLUE, CFNOW) .
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 ===== Human-Constrained Counterfactuals ===== ===== Human-Constrained Counterfactuals =====
 +{{:courses:xai:winner.png?30|}}
  
 Train a classifier on a tabular dataset (e.g., Breast Cancer, Adult, Heart Disease), generate counterfactual explanations for selected instances without any constraints using (ACFX, DICE, CLUE, CFNOW) , then regenerate counterfactuals while forbidding changes to sensitive or immutable features (e.g., age, gender, race, marital status, education). Compare both sets of explanations in a before/after table. Describe how the constraints impact the plausibility, fairness, and actionability of the results. Train a classifier on a tabular dataset (e.g., Breast Cancer, Adult, Heart Disease), generate counterfactual explanations for selected instances without any constraints using (ACFX, DICE, CLUE, CFNOW) , then regenerate counterfactuals while forbidding changes to sensitive or immutable features (e.g., age, gender, race, marital status, education). Compare both sets of explanations in a before/after table. Describe how the constraints impact the plausibility, fairness, and actionability of the results.
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 +===== TSProto for Colab =====
  
 +Try to run tutorials of [[https://tsproto.readthedocs.io/en/latest/|TSProto]] in Colab (it will require some code tweeking/requirements adjustment to work with numpy 2.0.0.
 +Once done, create a pull request to include the changes in the current implementation of TSProto
  
  
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