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Lee, Quentin (author)
The state-of-the-art shows the potential of chatbots and other Machine Learning (ML) models to perform many tasks of high quality. Especially chatbots are already used by many companies to assist their customer service. However, chatbots will likely never be able to perform all tasks perfectly. Therefore, it is still the question whether such a...
master thesis 2022
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Chen, Dina (author)
Recent works explain the DNN models that perform image classification tasks following the "attribution, human-in-the-loop, extraction" workflow. However, little work has looked into such an approach for explaining DNN models for language or multimodal tasks. To address this gap, we propose a framework that explains and assesses the model...
master thesis 2022
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Wang, Wenhui (author)
Data drift refers to the variation in the production data compare to the training data and sometimes the machine learning model would decay because of it. Some machine learning models face the problem when in production: they receive drift data while there’s no ground truth to evaluate model performance, thus no way of determining the...
master thesis 2022
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Lammerts, Philippe (author)
Hate speech detection on social media platforms remains a challenging task. Manual moderation by humans is the most reliable but infeasible, and machine learning models for detecting hate speech are scalable but unreliable as they often perform poorly on unseen data. Therefore, human-AI collaborative systems, in which we combine the strengths of...
master thesis 2022
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Jongerius, Ricardo (author)
Online dating has become the most popular method of finding potential romantic partners. At the core of these platforms, there is a reciprocal recommender system which recommends users to other users on the platform. Breeze is an example of such a dating app, serving its users potential romantic partners every day in the hopes of sending them on...
master thesis 2022
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Chen, Xinyue (author)
While the performance of traditional confidence-based rejectors is heavily dependent on the calibration of the pretrained model, this study proposes the concept of feature-based rejectors and the whole pipeline where such rejector can be used in. Multiple design and development decisions along the implementation are discussed in the paper -...
master thesis 2022
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Biswas, Shreyan (author)
Explaining the behaviour of Artificial Intelligence models has become a necessity. Their opaqueness and fragility are not tolerable in high-stakes domains especially. <br/>Although considerable progress is being made in the field of Explainable Artificial Intelligence, scholars have demonstrated limits and flaws of existing approaches:...
master thesis 2022
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Zheng, Meng (author)
Machine learning models are so-called a "black box," which means people can not easily observe the relationship between the output and input or explain the reason for such results. In recent years, much work has been done on interpretable machine-learning, such as Shapley values, counterfactual explanations, partial dependence plots, or saliency...
master thesis 2022
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Palakodeti, Anitej (author)
Generating synthetic images has wide applications in several fields such as creating datasets for machine learning or using these images to investigate the behaviour of machine learning models. An essential requirement when generating images is to control aspects such as the entities or objects in the image. Controlling this helps in creating...
master thesis 2022
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WANG, Siwei (author)
master thesis 2022
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Wang, Zhen (author)
This thesis mainly studies the causality in natural language processing. Understanding causality is key to the success of NLP applications, especially in high-stakes domains. Causality comes in various perspectives such as enable and prevent that, despite their importance, have been largely ignored in the literature. In view of the lack of a...
master thesis 2022
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Vasilcoiu, Ana-Maria (author)
To encourage ethical thinking in Machine Learning (ML) development, fairness researchers have created tools to assess and mitigate unfair outcomes. However, despite their efforts, algorithmic harms go beyond what the toolkits currently allow to measure. Through 30 semi-structured interviews, we investigated whether data scientists are...
bachelor thesis 2022
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Biedma Nuñez, Pablo (author)
The increasing dangers of unfairness in machine learning (ML) are becoming a frequent subject of discussion, both, in academia and popular media. Recent literature focused on introducing and assessing algorithmic solutions to bias in ML. However, there is a disconnect between these solutions and practitioners' needs. By interviewing 30 ML...
bachelor thesis 2022
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Ezard, François (author)
Common sense knowledge (CSK) comes naturally to humans, but is very hard for computers to comprehend. However it is critical for machines to behave intelligently, and as such collecting CSK has become a prevalent field of research. Whilst a lot of research has been done to develop CSK acquisition methods, not much work has been done to survey...
bachelor thesis 2022
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Kuiper, Adrian (author)
Commonsense knowledge is the key of human intelligence in generalizing their knowledge to deal with complex tasks. Over the past years, a lot of research has been done in both natural language processing (NLP) and computer vision (CV) on leveraging commonsense knowledge to improve AI models. However, no systematic comparisons of existing work...
bachelor thesis 2022
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Pandey, Harshitaa (author)
Machine learning is still one of the most rapidly growing fields, and is used in a variety of different sectors such as education, healthcare, financial modeling etc(Jordan and Mitchell 2015). However, along with this demand for machine learning algorithms, there comes a need for ensuring that these algorithms are fair and contain little to no...
bachelor thesis 2022
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Noritsyna, Eva (author)
The ability to identify and mitigate various risks and harms of using Machine Learning models in industry is an essential task. Specifically because these may produce harmful outcomes for stakeholders, including unfair or discriminatory results. Due to this there has been substantial research into the concepts of fairness and its metrics, bias...
bachelor thesis 2022
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Rachwani, Sanjay (author)
Commonsense knowledge (CK) in artificial intelligence (AI), is an expanding field of research. Because CK is intrinsically implicit, current datadriven machine learning models are still far from competent compared to humans in commonsense reasoning tasks. To minimize the gap between machine learning models with the goal of artificial general...
bachelor thesis 2022
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Renţea, Ilinca (author)
Commonsense knowledge is information that all humans own and use to interpret common situations and react to them accordingly. This kind of information is necessary for the training of artificial intelligence models to reach a performance as close as possible to human performance. Researchers have developed methods that use crowdsourcing to...
bachelor thesis 2022
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Ziengs, Bart (author)
Interpretability of ML models and image recognition models specifaclly, is a increasing problem. In this thesis, the design and implementation of Brickroutine: a system that used a trained model, is presented. Using human annotations, semantic interpretations are given to image classification problems. By giving an iterative approach in terms of...
master thesis 2022
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