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--- courses:xai:p1_2025 [2025/11/20 12:34] – [Predictive multiplicity estimation] admin
+++ courses:xai:p1_2025 [2025/12/01 09:24] (current) – [Programming Assignment I] admin
@@ Line 3: / Line 3: @@
 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.
+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 =====
+{{: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) .
+**Extra**: Evaluate the counterfactual explanations.
+===== 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.
+Example: “A bank cannot change a customer’s age or marital status, but can suggest financial improvements.”
+**Extra**: Quantify how many counterfactuals become invalid or infeasible when constraints are applied.
+===== Correlation Problems in XAI =====
+Train a model on a dataset with correlated features (e.g., Breast Cancer, Adult, Heart Disease). Visualize the correlation structure, then generate explanations using two XAI methods (e.g., SHAP and LIME). Identify at least one case where the model attributes importance to a feature that is likely acting as a proxy for another correlated variable. Show before/after visualizations and briefly explain whether the explanation is genuinely meaningful or simply an artifact of feature correlation.
+**Extra**: Propose and implement one strategy to reduce misleading explanations (e.g., feature grouping, PCA, domain-level feature merging…).
+===== 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