Responsible Machine Learning
Informacje ogólne
Kod przedmiotu: | 2400-SZD-QPE-RML |
Kod Erasmus / ISCED: | (brak danych) / (brak danych) |
Nazwa przedmiotu: | Responsible Machine Learning |
Jednostka: | Wydział Nauk Ekonomicznych |
Grupy: |
Przedmioty WNE dla programu QPE w Międzydziedzinowej Szkole Doktorskiej (ZIP) |
Punkty ECTS i inne: |
(brak)
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Język prowadzenia: | angielski |
Rodzaj przedmiotu: | nieobowiązkowe |
Skrócony opis: |
The aim of the classes is to familiarize participants with the issues of responsible machine learning. This will allow for the correct design of the machine learning experiment. In particular, we will discuss the basic techniques of explainable machine learning and the measures used in the assessment of fairness. In addition to seminar-type meetings, examples using R and Python will be presented. |
Pełny opis: |
During this class we will discuss: • Basics of machine learning. • Global methods of explaining the model, such as permutation importance of variables, Partial dependence profiles. • Methods of local model explanation, such as Shapley values, LIME, Break-Down, Ceteris paribus. • Methods of fairness analysis of a model. • We will discuss examples of literary failures related to ML models. Apart from the seminar formula, students will prepare a short essay on cases of using responsible ML. This essay and its presentation will be the basis for the credit. Bibliography: Fairness and machine learning Limitations and Opportunities Solon Barocas, Moritz Hardt, Arvind Narayanan https://fairmlbook.org/ Explanatory Model Analysis Explore, Explain, and Examine Predictive Models. With examples in R and Python. Przemyslaw Biecek and Tomasz Burzykowski https://pbiecek.github.io/ema/ A Responsible Machine Learning Workflow with Focus on Interpretable Models, Post-hoc Explanation, and Discrimination Testing Navdeep Gill, Patrick Hall, Kim Montgomery, Nicholas Schmidt https://www.mdpi.com/2078-2489/11/3/137 |
Literatura: |
Fairness and machine learning Limitations and Opportunities Solon Barocas, Moritz Hardt, Arvind Narayanan https://fairmlbook.org/ Explanatory Model Analysis Explore, Explain, and Examine Predictive Models. With examples in R and Python. Przemyslaw Biecek and Tomasz Burzykowski https://pbiecek.github.io/ema/ A Responsible Machine Learning Workflow with Focus on Interpretable Models, Post-hoc Explanation, and Discrimination Testing Navdeep Gill, Patrick Hall, Kim Montgomery, Nicholas Schmidt https://www.mdpi.com/2078-2489/11/3/137 |
Efekty uczenia się: |
By the end of the course, the student will be familiar: - with basic machine learning techniques, - with the area of eXplainable Artificial Intelligence, - with the area of fairness and transparency of Machine Learning. |
Metody i kryteria oceniania: |
By project. The course grade will be based on the preparation of a use case for Responsible ML, which will be described in the form of a short essay in an open ebook. See for example https://pbiecek.github.io/xai_stories/. The readability of the description, relevance for ML modelling and innovation related to new RML applications will be assessed. |
Właścicielem praw autorskich jest Uniwersytet Warszawski.