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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

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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

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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

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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

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Using psychological characteristics of situations for social situation comprehension in support agents

Kola, I. (author), Jonker, C.M. (author), Riemsdijk, M. Birna van (author)

Support agents that help users in their daily lives need to take into account not only the user’s characteristics, but also the social situation of the user. Existing work on including social context uses some type of situation cue as an input to information processing techniques in order to assess the expected behavior of the user. However,...

journal article 2023

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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

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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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The Influence of Adapting an Agent’s Explanation Style to a Human Team Leader Role on Human-Agent Teamwork during a Simulated Search and Rescue Task

Kap, Ryan (author)

Communication is one of the main challenges in Human-Agent Teams (HATs). An important aspect of communication in HATs is the use of explanation styles. This thesis examines the influence of an explainable agent adapting its explanation style to a supervising human team leader on team performance, trust, situation awareness, collaborative fluency...

master thesis 2022

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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

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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

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Using Human Workload to Adjust Agent Explanations in Human-agent Teamwork

LEI, Zhiqiang (author)

Artificial intelligence systems assist humans in more and more cases. However, such systems' lack of explainability could lead to a bad performance of the teamwork, as humans might not cooperate with or trust systems with black-box algorithms opaque to them. This research attempts to improve the explainability of artificial intelligence systems...

bachelor thesis 2022

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The Impact of Tailoring Agent Explanations According to Human Performance on Human-AI Teamwork

Parlar, Can (author)

Nowadays, artificial intelligence (AI) systems are embedded in many aspects of our lives more than ever before. Autonomous AI systems (agents) are aiding people in mundane daily tasks, even outperforming humans in several cases. However, agents still depend on humans in unexpected circumstances. Thus, the main goal of these agents has...

bachelor thesis 2022

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Domain-Knowledge-Driven Explainable Product Quality Prediction: Using prior knowledge to improve explanations of quality prediction models

Bos, Thomas (author)

Explainable artificial intelligence has in recent years allowed us to investigate how many machine learning methods are creating its predictions. This is especially useful in scenarios where the goal is not to predict a variable, but to explain what influences that variable. However, the methods that have been created thus far do not focus on...

master thesis 2022

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Putting explainable AI in context: institutional explanations for medical AI

Theunissen, M.R. (author), Browning, Jacob (author)

There is a current debate about if, and in what sense, machine learning systems used in the medical context need to be explainable. Those arguing in favor contend these systems require post hoc explanations for each individual decision to increase trust and ensure accurate diagnoses. Those arguing against suggest the high accuracy and...

journal article 2022

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Using human-in-the-loop and explainable AI to envisage new future work practices

Tsiakas, K. (author), Murray-Rust, D.S. (author)

In this paper, we discuss the trends and challenges of the integration of Artificial Intelligence (AI) methods in the workplace. An important aspect towards creating positive AI futures in the workplace is the design of fair, reliable and trustworthy AI systems which aim to augment human performance and perception, instead of replacing them...

conference paper 2022

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Defining Explanation and Explanatory Depth in XAI

Buijsman, S.N.R. (author)

Explainable artificial intelligence (XAI) aims to help people understand black box algorithms, particularly of their outputs. But what are these explanations and when is one explanation better than another? The manipulationist definition of explanation from the philosophy of science offers good answers to these questions, holding that an...

journal article 2022

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The influence of interdependence and a transparent or explainable communication style on human-robot teamwork

Verhagen, R.S. (author), Neerincx, M.A. (author), Tielman, M.L. (author)

Humans and robots are increasingly working together in human-robot teams. Teamwork requires communication, especially when interdependence between team members is high. In previous work, we identified a conceptual difference between sharing what you are doing (i.e., being transparent) and why you are doing it (i.e., being explainable). Although...

journal article 2022

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Unraveling Gap Selection Process during Discretionary Lane Changing by Vehicle Class

Sharma, Salil (author), van Lint, J.W.C. (author), Tavasszy, Lorant (author), Snelder, M. (author)

This paper studies and compares the gap selection process of multiple vehicle classes (passenger cars, delivery vans, and trucks) within their discretionary lane changing activities. Given a trajectory or a sequence of gap selection decisions, we aim to predict whether a vehicle will change or keep a lane. For this purpose, we use a large...

journal article 2022

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The FATE System Iterated: Fair, Transparent and Explainable Decision Making in a Juridical Case

de Boer, Maaike H.T. (author), Vethman, Steven (author), Bakker, R.M.B. (author), Adhikari, Ajaya (author), Marcus, Michiel (author), de Greeff, Joachim (author), van der Waa, J.S. (author), van Zoelen, E.M. (author), Kamphorst, Bart (author)

The goal of the FATE system is decision support with use of state-of-the-art human-AI co-learning, explainable AI and fair, secure and privacy-preserving usage of data. This AI-based support system is a general system, in which the modules can be tuned to specific use cases. The FATE system is designed to address different user roles, such as...

journal article 2022

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Explainable AI: A Proof of Concept Demonstration in Financial Transaction Fraud Detection using TreeSHAP & Diverse Counterfactuals

BALAKRISHNAN SELVAKUMARAN, PRATHEEP KUMAAR (author)

The European Commission recently published a proposal for an Artificial Intelligence (AI) act that requires the development of trustworthy AI systems for European Union markets. The proposal clearly mentions that AI systems should make use of Explainable AI tools to increase transparency and interpretability.Financial institutions in the...

master thesis 2021