IIS Wiki - courses:xai

lab1

Anonymous (anonymous@undisclosed.example.com) — 2024-11-25T07:33:10+00:00

Lab 1: Introduction to working environment, basics of data manipulation and visualization Resources * Google Colab Notebook: Colab * Datasets: * Adult Dataset: * Jigsaw dataset * Additional reading: * Google Deveoper ML course: Fairness * A Framework for Understanding Sources of Harm throughout the Machine Learning Life Cycle * Measurement and Mitigation of Bias in Artificial Intelligence: A Narrative Literature Review for Regulatory Science * A Survey on Bias and …

lab2

Anonymous (anonymous@undisclosed.example.com) — 2024-10-10T08:26:13+00:00

Lab2: Inherently interpretable models Resources * Colab: Colab * Additional reading: * Interpretable ML: Chapter 5 * Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead * Additional watching: * Nice video explaining how to obtain standard errors of LR estimates * Tools: * Pyro * PGMPy * CausalNex * ProbLog -> Interested? More on that at: KnAIS

lab3

Anonymous (anonymous@undisclosed.example.com) — 2024-10-20T16:34:08+00:00

Lab3: Inherently interpretable models Resources * Colab: Colab * Additional reading: * Interpretable ML: Chapter 5 * Accurate Intelligible Models with Pairwise interactions * Predictive learning via rule ensembles -- RuleFit hteory * Neural Networks are Decision Trees * Additional watching: * Recap on Gradient Boosting

lab4

Anonymous (anonymous@undisclosed.example.com) — 2023-11-06T09:08:04+00:00

Lab4: Global model-agnostic approaches Resources * Colab: Colab * Additional reading: * Interpretable ML: Chapter 8 * Examples are not enough, learn to criticize! criticism for interpretability

lab5

Anonymous (anonymous@undisclosed.example.com) — 2023-11-20T07:50:11+00:00

Lab5: Local model-agnostic approaches Resources * Colab: Colab * Additional reading: * Interpretable ML: Chapter 9 * Interpreting Machine Learning Models With SHAP * (LIME) "Why Should I Trust You?": Explaining the Predictions of Any Classifier * (KernelSHAP) A Unified Approach to Interpreting Model Predictions * (TreeSHAP) Consistent Individualized Feature Attribution for Tree Ensembles

lab6

Anonymous (anonymous@undisclosed.example.com) — 2023-11-20T07:49:57+00:00

Lab6: Local model-agnostic approaches Resources * Colab: Colab * Additional reading: * Interpretable ML: Chapter 9 * TBD

lab7

Anonymous (anonymous@undisclosed.example.com) — 2023-12-11T08:44:05+00:00

Lab7: Counterfactual explanations Resources * Colab: Colab * Additional reading: * Interpretable ML: Chapter 9 * Counterfactual explanations and how to find them: literature review and benchmarking

lab8

Anonymous (anonymous@undisclosed.example.com) — 2023-12-18T08:32:30+00:00

Lab8: Evaluation of explanations Resources * Colab: Colab * Additional reading: * Interpretable ML: Chapter 3 * Counterfactual explanations and how to find them: literature review and benchmarking

lab9

Anonymous (anonymous@undisclosed.example.com) — 2026-01-05T13:44:18+00:00

Lab9: Explainability of DNN Resources * Colab: Colab * Additional reading: * ProtoPNet * SENN * GradCAM * The building blocks of intrepretability -- overview of feature visualization with Colab examples * Integrated gradients --overview with Colab examples * AIRA seminnar:

p1

Anonymous (anonymous@undisclosed.example.com) — 2023-12-04T09:18:27+00:00

Programming Assignment I Select one of the assignments below. Assignments marked with can be extended as a continution in Programming Assgnment II. Projects marked with can be (with some additional work and depending on the results) published as scientific papers.

p1_2024

Anonymous (anonymous@undisclosed.example.com) — 2024-11-22T12:35:28+00:00

p1_2025

Anonymous (anonymous@undisclosed.example.com) — 2025-12-01T09:24:00+00:00

p2

Anonymous (anonymous@undisclosed.example.com) — 2024-01-14T22:02:00+00:00

Programming Assignment II Select one of the assignments below. Projects marked with can be (with some additional work and depending on the results) published as scientific papers. Counterfactuals Implement and evaluate two selected CF methods with three selected CF evaluation metric.

p2_2024

Anonymous (anonymous@undisclosed.example.com) — 2026-01-19T07:23:44+00:00

Programming Assignment II Select one of the assignments below. Assignments marked with can be extended as a continution in Programming Assgnment II. Projects marked with can be (with some additional work and depending on the results) published as scientific papers.

start

Anonymous (anonymous@undisclosed.example.com) — 2025-12-17T10:06:44+00:00

Explainable Artificial Intelligence * Course ID: WFAIS.IF-XG325.0 * Room: G-1-08 * Teachers: * Lectures: dr inż. Szymon Bobek (SBK) * Labs: * Mondays dr inż. Szymon Bobek (SBK) * Wednesdays Sabri Manai, PhD Candidate (SMI) Lectures * [07.10.2025] [SBK] Lecture plan, rules, organizaiton of classes