document

Ecology of generative AI: experiencing the physical side of AI to gain an understanding of the ecosystem behind your screen

Brouwer, Lucie (author)

master thesis 2024

document

The impact of expressing emotion within explainable AI in human-agent teamwork

Wang, Sunwei (author)

With the increasing development of artificial intelligence (AI), there is a more significant opportunity for humans and agents to collaborate in teamwork. In Human-Agent Teamwork (HAT) settings, collaboration requires communication, and the agent displaying emotion can impact how human teammates communicate and work together with the agent. This...

master thesis 2024

document

Counterfactual Explanations of Learned Reward Functions

Wehner, Jan (author)

Learning rewards from humans is a promising approach to aligning AI with human values. However, methods are not able to consistently extract the correct reward functions from demonstrations or feedback. To allow humans to understand the limitations and misalignments of a learned reward function we adopt the technique of counterfactual...

master thesis 2023

document

Instance Attribution in Information Retrieval: Identifying and Selecting Influential Instances with Instance Attribution for Passage Re-Ranking

Hacipoğlu, Sara (author)

The complexity of deep neural rankers and large datasets make it increasingly more challenging to understand why a document is predicted as relevant to a given query. A growing body of work focuses on interpreting ranking models with different explainable AI methods. Instance attribution methods aim to explain individual predictions of machine...

master thesis 2023

document

Exploring the effect of the information amount in explanation on different gaming expertise levels

Zhou, Jing (author)

Explainable AI (XAI) has gained increasing attention from more and more researchers with an aim to improve human interaction with AI systems. In the context of human-agent teamwork (HAT), providing explainability to the agent helps to increase shared team knowledge and belief, therefore improving overall teamwork. With various backgrounds and...

master thesis 2023

document

Data Model for Computer Vision Explainability, Fairness, and Robustness

Karnani, Simran (author)

In recent years, there has been a growing interest among researchers in the explainability, fairness, and robustness of Computer Vision models. While studies have explored the usability of these models for end users, limited research has delved into the challenges and requirements faced by researchers investigating these requirements. This study...

master thesis 2023

document

Explainable Neural Networks for Incipient Slip Sensing in Robot Tactile Learning

Polak, Max (author)

Incipient slip detection plays an important role in human and robotic grasping. With the growing use of deep learning in vision-based tactile sensing, the black-box nature of these deep neural networks (DNNs) makes it difficult to analyze, debug, and validate their behavior and learned patterns. To fill this gap, eXplainable AI (XAI) methods...

master thesis 2023

document

Maxplain – Value-based Evaluation of Explainable AI Techniques

Deb, Sreeparna (author)

A 2022 Harvard Business Review report critically examines the readiness of AI for real-world decision-making. The report cited several incidents, like an experimental healthcare chatbot suggesting a mock patient commit suicide in response to their distress or when a self-driving car experiment was called off after it resulted in the death of a...

master thesis 2023

document

An Explainable Network-wide Metro Passenger Delay Prediction Model

Cheng, Yuxing (author)

master thesis 2023

document

Interactive Model Explanations for Greater Intelligibility

Aishwarya, Nilay (author)

As AI is progressively incorporated into several spheres of society. This rapid growth has also brought a lot of challenges such as discriminating or skewed results and a lack of accountability. To address these challenges, there is a growing interest in Human-AI teams where AI-assisted decision-making includes humans in the loop. This approach...

master thesis 2023

document

How truthful explanation of a conceder negotiation agent affects human trust, compared to a neutral one

Germanov, Pavel (author)

Trust in negotiation agents plays a crucial role in their adoption and utilization. However, there is not enough research on what factors influence it. This paper aims to investigate how different explanations of a negotiation agent’s strategy affect human trust and decision-making. Specifically, it compares the effects of a truthful explanation...

bachelor thesis 2023

document

The Effect of Different Explanations of Conceder Negotiation Style on the Trust between Humans and Agents

Hasdemir, Deniz Tan (author)

Negotiations have an essential role in our lives as they help us to find mutually beneficial solutions and resolve conflicts. It leads to effective communication and collaboration between the involved parties. Negotiation among parties has high importance to have an outcome that is suitable for all. In such scenarios, negotiation agents can be...

bachelor thesis 2023

document

Elucidating a ‘black-box’ transcends explaining the algorithm: Exploring Explainable AI (XAI) as a way to address AI implementation challenges in the Dutch public sector

Rajiv, Miny (author)

Responding to the trend of increasing use of artificial intelligence (AI), we need to ensure applications of AI are designed, implemented, utilised and evaluated in a careful manner. Explainable AI, or XAI, meaning; - given a certain audience, the details and reasons of both technical processes of the algorithm-support system and the reasoning...

master thesis 2023

document

Explainable AI for metabolic engineering

van Graft, Tijmen (author)

Metabolic engineering is an important field in biotechnology, aimed at optimizing cellular processes to produce desired compounds. In this thesis, we focus on predicting the metabolome from the proteome, as understanding this relationship is crucial for understanding cellular metabolism. We investigate the usage of additional biological...

master thesis 2023

document

Extracting insights from black box neural networks for network-wide traffic predictions

Wang, Heqi (author)

Accurate and trustworthy short-term traffic prediction is crucial in the modern world for the comfort of drivers and decision-makers as it is used to improve the performance of traffic management systems, lessen congestion, increase safety, and shorten journey times. It is possible to discover useful information for network transportation...

master thesis 2023

document

How do different explanation presentation strategies of feature and data attribution techniques affect non-expert understanding?: Explaining Deep Learning models for Fact-Checking

Singh, Shivani (author)

The goal of this paper is to examine how different presentation strategies of Explanainable Artificial Intelligence (XAI) explanation methods for textual data affect non-expert understanding in the context of fact-checking. The importance of understand- ing the decision of an Artificial Intelligence (AI) in human-AI interaction and the need for...

bachelor thesis 2023

document

A Comparison of Instance Attribution Methods: Comparing Instance Attribution Methods to Baseline k-Nearest Neighbors Method

de Kruif, Evan (author)

In this research, a comparison between different Instance Attribution (IA) methods and k-Nearest Neighbors (kNN) via cosine similarity is conducted on a Natural Language Processing (NLP) machine learning model. The format in which the comparison is made is by way of a human survey and automated similarity comparisons of representative vectors....

bachelor thesis 2023

document

Situating Explainable AI in the socio-technical context: A system safety inspired approach to operationalizing explainability

Kempen, Alexander (author)

Explainable AI is the field concerned with trying to make AI understandable to humans. While efforts have resulted in significant improvement in research and practical methods of Explainable AI, there is an urgent need for additional research and empirical studies. The academic research gaps identified in this thesis show that Explainable AI is...

master thesis 2022

document

Explainable Reinforcement Learning in Flight Control through Reward Decomposition

Ferreira Lemos, André (author)

Even though Deep Reinforcement Learning (DRL) techniques have proven their ability to solve highly complex control tasks, the opaqueness and inexplicability associated with these solutions many times stops them from being applied to real flight control applications. In this research, reward decomposition explanations are used to tackle this...

master thesis 2022

document

CHIME: Causal Human-In-the-Loop Model Explanations

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