Explainable & Trustworthy AI
Artificial Intelligence consists of complex and sophisticated algorithms that sometimes make it difficult for humans to understand and interpret the decisions or suggestions of the AI system. This course on Explainable AI looks into the different aspects related to (creating) trust in AI.
Practical information:
Want to register?
- Register until: 20 Mar 2023
- Prerequisites: Higher education in computer science or equivalent experience programming experience with Python or related programming language
- Price: €1.650 on campus / €1.400 online
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Artificial Intelligence (AI) has come a long way since its first use and application many decades ago. The use of AI and Machine Learning have seen an immense uptake in the 21st century and the techniques are successfully applied to a wide variety of problems, both in academia, private and public industry. As this domain became more and more established in recent years, new challenges arose because the rationale and outcomes cannot always be followed by people.
Explainable AI puts the following properties of trust in AI on the foreground:
- Gaining trust by explaining for example the characteristics of AI output.
- By explaining an AI technique understanding will increase, allowing to investigate if the technique can be transferred to another domain or problem.
- Informing a user about the workings of an AI model so that there is no misinterpretation.
- Confidence of users can be established by using AI models that are explainable, stable but also robust.
- When explaining AI models issues concerning privacy awareness come into play. Private data should not be exposed by the models.
- It is important that actions can be explained. How have we come to specific outcomes and how could we change them?
- Nowadays, a wide variety of people from different background come into contact AI, it is important that they all understand why the system is behaving in such a manner and offer explanations tailored to their needs.
Programme
Introduction, 20 March 2023
In this first module, we give a short recap of the basics, followed by the explanation of some general terms that are used in the domain of explainable and trustworthy Artificial Intelligence. This introduction will end with the definition of the challenges within this domain.
- Recap the basics: AI, ML and statistics
- Different types of ML: white-box & black-box
- Interpretability vs Explainability
- Human Uncertainty vs Model Uncertainty
- Challenges
Teacher: prof. dr. ir. Sofie Van Hoecke, Department of Electronics and Information Systems, Ghent University & prof. dr. Femke Ongenae, Department of Information Technology, Ghent University
Global vs Local Interpretability, 27 March 2023
Machine learning systems help us connect cause and effect in complex data sets. How we can make those interpretations depends on the type of algorithm used. The level of interpretability we can reach will also differ depending on whether we look at how the model predicts in general, versus how a specific prediction of the model was computed. When interpretability is good enough, we can use hypothetical questions like “What if instead this would be the case?”, adding to the usefulness of your system. This module explains how to increase and measure those interpretabilities.
- Global Interpretability
- Local Interpretability
- Counterfactuals
- Model distillation
- Dependency plots
- Evaluation methods
Teacher: Arne Gevaert, Department of Applied Mathematics, Computer Science & Statistics, Ghent University
White box models, 17 April 2023
In this module, focus is given to white box models. While black box models offer higher accuracy, white box models are easier to explain and to interpret, unfortunately this leads to a lesser predictive capacity. In the area of white box models, several different approaches will be highlighted:
- Linear Regression
- Decision Trees and Rule Sets
- Generalized Additive Models (GAMs)
Teacher: dr. Daniel Peralta Cámara, Department of Information Technology, Ghent University
Saliency mapping, 24 April 2023
To help explain how a neural network reached a certain conclusion, certain visualizations of its reasoning can be useful. One type of visualization is a heatmap showing which areas of a photo contribute most to how a system has labeled it. These visualizations are explained in this session.
- Convolutional Neural Networks (CNNs) & interpretation
- Backpropagation
- Other gradient-based methods
Teacher: dr. Arne Gevaert, Department of Applied Mathematics, Computer Science & Statistics, Ghent University
Hybrid AI, 8 May 2023
The oldest forms of machine learning entail rule engines that were hand programmed. Newer forms entail algorithms searching for connections themselves. The first are great in explaining how they reach their conclusions. The latter sometimes give superior predictions, being a lot less brittle, but lack that explainability. To get the best of both worlds, these approaches are sometimes combined. Moreover, allowing an expert to guide a machine learning system can sometimes lead to yet again superior predictions. This module explains how.
- Data-driven vs expert-based approaches
- Finding synergies in data-driven and expert-based approaches
- Combining expert knowledge and machine learning
Teacher: prof. dr. ir. Sofie Van Hoecke & prof. dr. Femke Ongenae, Department of Information Technology, Ghent University
Robustness, 15 May 2023
The output of a machine learning system depends on the data used as input. Often the needed amount and structure of that data is overlooked. However, machine learning systems can be combined to generate additional data or to finetune each other. Nevertheless, malicious additions to your training data can corrupt your system and even a well-trained system can be deceived. This module explains these issues and what you can do about them.
- Data quality & quantity
- Adversarial learning
- Poisoning attacks & defenses
- Learning theory
Teacher: Jonathan Peck, Department of Applied Mathematics, Computer Science & Statistics, Ghent University
Online & Transfer Learning, 22 May 2023
Training machine learning systems can be done before use, i.e. when training it on a stack of pictures first and asking it to make sense of new pictures later. However, it can also be done during use. In the latter scenario the system gets updated whilst it is being used. Sometimes this is necessary because training data is (partially) becoming available after commissioning of the system. Sometimes a system is pretrained on one dataset and the developer wants to retrain the system in order to solve another but related problem, i.e. using a machine vision system that is trained to detect cats to now detect dogs. The developer thus leverages the effort put into the training of the earlier system, hence requiring less training time for the novel system. These and other relations between datasets, their application in training models and the problems we solve with those will be explained in this module.
- Online learning
- Change detection
- Transfer learning & domain adaptation (foundation models)
Teacher: Jan Van Looy & Matthias Feys, ML6
Bias & Fairness, 5 June 2023
When training machine learning systems, the training data can be biased, leading to unwanted outcomes, i.e. an HR system trained on old hospital personnel data now stating that women might be unlikely good candidates for doctor positions. This module will explain these issues, how to avoid them, how to measure bias and what the limitations of avoiding it are.
- Various notions of fairness & impossibility theorem
- Different types of bias & methods to debias
- Ethical guidelines
- Learning fair models
- Uncovering model bias
Teacher: Prof. Tijl De Bie, Department of Electronics and Information Systems, Ghent University
Privacy, 12 June 2023
Sometimes the quality of machine learning system outputs and privacy are at odds and need to be balanced. However, there are techniques that allow the training of machine learning systems on privacy sensitive data, without exposing the data itself. Those techniques and relevant regulation on these practices are explained in this module.
- Pseudonimization
- K-anonymity
- Differential privacy
- Regulation
Teacher: Prof. Tijl De Bie, Department of Electronics and Information Systems, Ghent University
Use cases, 19 June 2023
During this module, some specific use cases in the domain of Explainable and Trustworthy AI will be discussed.
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