document

Finding Shortcuts to a black-box model using Frequent Sequence Mining: Explaining Deep Learning models for Fact-Checking

Smit, Jean-Paul (author)

Deep-learning (DL) models could greatly advance the automation of fact-checking, yet have not widely been adopted by the public because of their hard-to-explain nature. Although various techniques have been proposed to use local explanations for the behaviour of DL models, little attention has been paid to global explanations. <br/>In response,...

bachelor thesis 2023

document

Evaluating Feature Attribution Methods: an Usecase on a Neural Fact-checking Model

Simons, Annabel (author)

In today's society, claims are everywhere, in the online and offline world. Fact-checking models can check these claims and predict if a claim is true or false, but how can these models be checked? Post-hoc XAI feature attribution methods can be used for this. These methods give scores indicating the influence of the individual tokens on the...

bachelor thesis 2023

document

Explainable Artificial Intelligence for human-AI collaboration

van der Waa, J.S. (author)

As a society, we have come to notice the influence and impact Artificially Intelligent (AI) agents have on the way we live our lives. For these AI agents to support us both effectively and responsibly, we require an understanding on how they make decisions and what the consequences are of these decisions. The research _field of Explainable...

doctoral thesis 2022

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Model-specific Explainable Artificial Intelligence techniques: State-of-the-art, Advantages and Limitations

Khan, Arghem (author)

Artificial Intelligence (AI) and Machine learning (ML) applications are being widely used to solve different problems in different sectors. These applications have enabled the human-effort and involvement to be very low. The AI/ML systems<br/>make their own predictions and do not require a great deal of human help. However, over the last few...

bachelor thesis 2022

document

Explainable Artificial Intelligence Techniques for the Analysis of Reinforcement Learning in Non-Linear Flight Regimes

de Haro Pizarroso, Gabriel (author)

Reinforcement Learning is being increasingly applied to flight control tasks, with the objective of developing truly autonomous flying vehicles able to traverse highly variable environments and adapt to unknown situations or possible failures. However, the development of these increasingly complex models and algorithms further reduces our...

master thesis 2022

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Investigation of the evaluation techniques and tools used for model-specific XAI models

Marbot, Tanguy (author)

The spread of AI techniques has lead to its presence in critical situations, with increasing performance that can compromise on its understanding. Users with no prior AI knowledge rely on these techniques such as doctors or recruiters with a need for transparency and comprehensibility of the mechanisms. The advent of Explainable Artificial...

bachelor thesis 2022

document

Investigation and Comparison of Evaluation Methods of Model-Agnostic Explainable AI Models

Oedayrajsingh Varma, Vanisha (author)

Many artificial intelligence (AI) systems are built using black-box machine learning (ML) algorithms. The lack of transparency and interpretability reduces their trustworthiness. In recent years, research into explainable AI (XAI) has increased. These systems are designed to tackle common ML issues such as trust, accountability, and transparency...

bachelor thesis 2022

document

Explainable Artificial Intelligence (XAI) Techniques - A Review and Case Study

Lee Kaijen, Kaijen (author)

The significant progress of Artificial Intelligence (AI) and Machine Learning (ML) techniques such as Deep Learning (DL) has seen success in their adoption in resolving a variety of problems. However, this success has been accompanied by increasing model complexity resulting in a lack of transparency and trustworthiness. Explainable Artificial...

bachelor thesis 2022

document

Quantifying the Endogenous Domain and Model Shifts Induced by the DiCE Generator

Buszydlik, Aleksander (author)

Algorithmic recourse aims to provide individuals affected by a negative classification outcome with actions which, if applied, would flip this outcome. Various approaches to the generation of recourse have been proposed in the literature; these are typically assessed on statistical measures such as the validity of generated explanations or their...

bachelor thesis 2022

document

Quantifying the Endogenous Domain and Model Shifts Induced by the CLUE Recourse Generator

Dobiczek, Karol (author)

Employing counterfactual explanations in a recourse process gives a positive outcome to an individual, but it also shifts their corresponding data point. For systems where models are updated frequently, a change might be seen when recourse is applied, and after multiple rounds, severe shifts in both model and domain may occur. Algorithmic...

bachelor thesis 2022

document

Automated Defect Analysis using Optical Sensing and Explainable Artificial Intelligence for Fibre Layup Processes in Composite Manufacturing

Meister, S. (author)

In modern aircraft, structural lightweight composite components are increasingly<br/>used. To manufacture these components in a costeffective way, robust production systems for the manufacturing of complex fibre composite components are necessary. A rather novel but already established process for fibre material deposition is the Automated Fibre...

doctoral thesis 2022

document

Using Explainable Artificial Intelligence to Improve Transparency of Reinforcement Learning for Online Adaptive Flight Control: Breaking Open the Black Box

van Zijl, Job (author)

Deep Reinforcement Learning (DRL) shows great potential for flight control, due to its adaptability, fault-tolerance, and as it does not require an accurate system model. However, these techniques, like many machine learning applications, are considered black-box as their inner workings are hidden. This paper aims to break open the black box of...

master thesis 2022

document

Event-triggered constrained control using explainable global dual heuristic programming for nonlinear discrete-time systems

Sun, B. (author), van Kampen, E. (author)

This paper develops an event-triggered optimal control method that can deal with asymmetric input constraints for nonlinear discrete-time systems. The implementation is based on an explainable global dual heuristic programming (XGDHP) technique. Different from traditional GDHP, the required derivatives of cost function in the proposed method...

journal article 2022

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Explainable artificial intelligence in forensics: Realistic explanations for number of contributor predictions of DNA profiles

Veldhuis, M.S. (author), Ariëns, Simone (author), Ypma, Rolf J.F. (author), Abeel, T.E.P.M.F. (author), Benschop, Corina C.G. (author)

Machine learning obtains good accuracy in determining the number of contributors (NOC) in short tandem repeat (STR) mixture DNA profiles. However, the models used so far are not understandable to users as they only output a prediction without any reasoning for that conclusion. Therefore, we leverage techniques from the field of explainable...

journal article 2022

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Classifying Human Pilot Skill Level Using Deep Artificial Neural Networks

de Jong, Martijn (author)

To fully optimize the synergy between human operators and machines in modern day’s highly automated vehicle control tasks, a real-time quantitative feedback of skill level is required. Direct feedback of skill level could be used to enable scalable levels of autonomy of the controlled system, or to provide a warning when sudden skill level...

master thesis 2021

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Enabling Human-In-The-Loop Interpretability Methods of Machine Learning Models: The Case of Bird Species Identification

Zeng, Wei (author)

To study how to involve the end-users in the development of machine learning explainability, this project has chosen the context of bird species identification. It intends to develop a platform where the end-users can learn bird knowledge while contributing to building the explainability of machine learning models. Among all the methods that...

master thesis 2021

document

Explainable artificial intelligence in forensics: realistic explanations for number of contributor predictions of DNA profiles

Veldhuis, Marthe (author)

Using machine learning to determine the number of contributors (NOC) in short tandem repeat (STR) mixture DNA profiles has been shown to obtain good accuracy. However, the models used so far are not transparent to users as they only output a prediction without any reasoning for that conclusion. Therefore, we leverage techniques from the field of...

master thesis 2021

document

Evaluating XAI: A comparison of rule-based and example-based explanations

van der Waa, J.S. (author), Nieuwburg, Elisabeth (author), Cremers, Anita (author), Neerincx, M.A. (author)

Current developments in Artificial Intelligence (AI) led to a resurgence of Explainable AI (XAI). New methods are being researched to obtain information from AI systems in order to generate explanations for their output. However, there is an overall lack of valid and reliable evaluations of the effects on users' experience of, and behavior in...

journal article 2021

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Interactive Natural Language Technology for Explainable Artificial Intelligence

Alonso, Jose M. (author), Barro, Senén (author), Bugarín, Alberto (author), van Deemter, Kees (author), Gardent, Claire (author), Gatt, Albert (author), Reiter, Ehud (author), Sierra, Carles (author), Theune, Mariët (author), Tintarev, N. (author), Yano, Hitoshi (author), Budzynska, Katarzyna (author)

We have defined an interdisciplinary program for training a new generation of researchers who will be ready to leverage the use of Artificial Intelligence (AI)-based models and techniques even by non-expert users. The final goal is to make AI self-explaining and thus contribute to translating knowledge into products and services for economic...

conference paper 2021

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Cross-evaluation of a parallel operating SVM – CNN classifier for reliable internal decision-making processes in composite inspection

Meister, S. (author), Wermes, Mahdieu (author), Stueve, J. (author), Groves, R.M. (author)

In the aerospace industry, automated fibre laying processes are often applied for economical composite part fabrication. Unfortunately, the current mandatory visual quality assurance process takes up to 50% of the entire manufacturing time. An automised classification of manufacturing deviations using Neural Networks potentially improves the...

journal article 2021