Proseminar Wintersemester 2020/2021
Machine Learning
Prof. Dr. Katharina Morik
Informatik LS8
Trustworthy Machine Learning
Machine learning (ML) is a driving force for many successful applications in Artificial Intelligence. Machine learning is not just a class of algorithms but there are long and nested sequences of algorithms and, at the same time, algorithms are built upon other algorithms from libraries or tools. This makes it hard for users to understand machine learning models.
One approach to allowing an understanding of machine learning is to explain the learned model. It has been pointed out that also the data which are used for training the model need a careful inspection. 8 of the selected papers for this seminar deal with explainability. Among them are, of course, those that explain deep neural networks
We want to include no only pure computer science views, but also look at what psychology, sociology and ethics have to say about AI and ML, in particular. A view of the broad area of ethics and bias is represented by 3 carefully selected papers.
Deep neural networks are particularly challenging our understanding. Their function approximation capabilities are so huge that they are hard to understand and control. A comprehensive sound paper on the trustworthiness of deep neural networks is split into three parts, so that three students could study this work. All parts only need the introduction as an additional read, not the other parts.
The impact of learned models on staff selection, sales, and popularity is tremendous. The bias in data lead to a bias in real-world life. Actually, biased data may do harm to the health and success of people.
Hence, the community of researchers in machine learning and other disciplines discuss how to establish fairness. 12 papers cover diverse aspects of fairness.
Date: Tuesdays, 14:15 - 16:00 h, online
Moodle Workspace
Topics, literature (excluding books) and dates:
Topic |
Publications |
Date |
Explainability |
When People and Algorithms Meet: User-Reported Problems in Intelligent Everyday Applications |
17.11.2020 |
Explainability |
A Survey of Methods for Explaining Black Box Models |
17.11.2020 |
Explainability |
Interpreting Classifiers by Multiple Views |
17.11.2020 |
Explainability |
Explainability Fact Sheets: A Framework for Systematic Assessment of Explainable Approaches |
24.11.2020 |
Explainability |
Datasheets for Datasets |
24.11.2020 |
Explainability |
Evolutionary Psychology and Artificial Intelligence: The Impact of AI on human behaviour |
24.11.2020 |
Ethics And Bias |
AI4People - An Ethical Framework for a Good AI Society: Opportunities, Risks, Principles and Recommendations |
01.12.2020 |
Ethics And Bias |
Semantics Derived Automatically from Language Corpora Contain Human-Like Bias |
01.12.2020 |
Ethics And Bias |
Wikipedia, Sociology and the Pitfalls of Big Data |
01.12.2020 |
Trustworthy Deep Neural Networks |
A Survey of Safety and Trustworthiness of Deep Neural Networks: Verification, Testing, Adversarial Attacks and Defence, and Interpretability -- Safety and Verification |
08.12.2020 |
Trustworthy Deep Neural Networks |
A Survey of Safety and Trustworthiness of Deep Neural Networks: Verification, Testing, Adversarial Attacks and Defence, and Interpretability -- Testing and Adversarial Attack |
08.12.2020 |
Trustworthy Deep Neural Networks |
A Survey of Safety and Trustworthiness of Deep Neural Networks: Verification, Testing, Adversarial Attacks and Defence, and Interpretability -- Interpretability |
08.12.2020 |
Explainability |
Methods for Interpreting and Understanding Deep Neural Networks |
15.12.2020 |
Explainability |
Evaluating the Visualization Of What A Deep Neural Network Has Learned |
15.12.2020 |
Trustworthy Deep Neural Networks |
Fairness Through Robustness: Investigating Robustness Disparity in Deep Learning |
15.12.2020 |
Fairness |
Fairness Constraints: A Flexible Approach for Fair Classification |
05.01.2021 |
Fairness |
Three Naive Bayes Approaches for Discrimination-Free Classification |
05.01.2021 |
Fairness |
Two-Sided Fairness for Repeated Matchings in Two-Sided Markets: A Case Study of a Ride-Hailing Platform |
05.01.2021 |
Fairness |
Fairness and Discrimination in Retrieval and Recommendation |
12.01.2021 |
Fairness |
Equity of Attention: Amortizing Individual Fairness in Rankings |
12.01.2021 |
Fairness |
Fairness-Aware Ranking in Search and Recommendation Systems with Applications to LinkedIn Talent Search |
12.01.2021 |
Fairness |
Fa*ir: A Fair Top-k Ranking Algorithm |
19.01.2021 |
Fairness |
Unbiased Learning-to-Rank with Biased Feedback |
19.01.2021 |
Fairness |
Fair Learning-to-Rank from Implicit Feedback |
19.01.2021 |
Fairness |
Fair-by-design Matching |
26.01.2021 |
Fairness |
Poisoning Attacks on Algorithmic Fairness |
26.01.2021 |
Explainability |
On cognitive preferences and the plausibility of rule-based models |
still available |