The effects of digital technologies on freedom and democracy have garnered increasing attention in recent years. Many have raised concerns about surveillance capitalism, technofeudalism, and general threats to constitutional democracies—with a special convergence on the worry that uncontrolled power of online platforms undermines people’s freedom. However, it remains unclear how ‘freedom’ should be understood, what the relation is between freedom and uncontrolled power, and to what extent these worries extend beyond online platforms. In this dissertation, I argue that these problems are best answered by appealing to a neo-republican account of freedom as non-domination, where ‘domination’ is understood as a condition of living under an agent’s uncontrolled power. In the context of AI systems used in core societal sectors such as healthcare, I show that domination of a system’s (in)direct end-users by the system’s developers occurs in at least three ways: (1) the distribution of decision-making power, (2) technical limitations of AI systems, and (3) underlying societal structures that empower developers and disempower end-users. To safeguard freedom in the digital age, I propose that AI development requires the explicit intention to 'design for non-domination'. This requires us to consider the broader societal contexts within which these systems operate, such as current regulatory initiatives and the political economy.@en

This research revolves around measuring the quality of arguments. High-quality arguments help in improving political discussions, resulting in better decision-making. Wachsmuth et al. developed a taxonomy breaking down argument quality into several dimensions. This work makes use of that taxonomy and combines it with modern NLP models. A cross-dataset examination of argument quality models was conducted. In particular, models were investigated on their generalizability between dimensions. Overall results show that there is no large difference in accuracy and agreement when models predict data of a quality dimension they were trained on, over dimensions they were not trained on. One can conclude that generalizations of argument quality dimensions with language models were not found. Nevertheless, qualitative analysis highlights findings that indicate some generalization to other dimensions.

Moral values influence humans in decision-making. Pluralist moral philosophers argue that human morality can be represented by a finite number of moral values, respecting the differences in moral views. Recent advancements in NLP show that language models retain a discernible level of knowledge in deontological ethics and moral norms of society. However, a model which can only decide either right or wrong cannot fully understand the diverse moral perspectives of humans.

We propose a moral sentence embedding space, which can encompass moral differences, through the state-of-the-art Contrastive Learning framework. We evaluate the moral embedding space both intrinsically and extrinsically via three tasks: classification, moral similarity, and visual analysis. We show that our moral embedding space understands the characteristics of each moral value. Our results also highlight that moral rhetoric is seldom explicit in the text, emphasizing the necessity of additional information such as moral labels.

In April 2020, a Dutch research team swiftly analyzed public opinions on COVID-19 lockdown relaxations. However, due to time constraints, only a small amount of opinion data could be processed. With the surge of popularity in the field of Natural Language Processing (NLP) and the arrival of tools like ChatGPT, a number of tasks involving Large Language Models (LLMs) have become easier. This study aims to address the effectiveness of these LLMs on stance detection using this COVID-19 opinion corpus. The corpus is chunked and sampled to be used as input for OpenAI's GPT-3.5-Turbo LLM. The machine-generated stances are then evaluated against multiple binary classification metrics. It is shown that these models perform very well in the field of stance detection, with an average F-score of 0.895. However, a significant number of misclassifications are observed in one dataset. Therefore we conclude that while LLMs offer valuable guidelines, it is still crucial to verify their outputs when dealing with complex or important public matters.

Meetings are the keystone of a good company. They allow for quick decision making, multiple-perspective problem solving and effective communication. However, most employees and managers have a negative view on the efficiency and quality of their meetings. High quality meetings where every participant feels equally heard and respected is crucial for having positive meeting sentiment within a company. One of the most influential aspects of meetings are speech overlaps. Overlaps range from short utterances such as backchannels, to follow up questions and clarifications, to complete interruptions. In non-competitive cases, the overlapped speaker feels that the other participants are listening and actively engaging with them during the meeting. In competitive cases, the overlapped speaker can feel interrupted and unimportant. Therefore, competitive overlaps often have a negative impact on the course of the discussion and the overlappee's meeting sentiment. In problematic cases, these overlaps should be reduced to a minimum. In order to do this, overlaps must be classified as either competitive or non-competitive. This paper proposes a novel approach to overlap classification, namely that of text-based classification through Large Language Models. Four different prompt designs are used and tested on the two best performing and publicly available models, GPT-3.5-turbo and GPT-4. The results show that the in-context learning approach using the GPT-4 model results in the most accurate classifications. When comparing the results to previous work, it is observed that the text-based GPT-4 model matches carefully engineered neural networks that even adopt a multi-modular approach.

Large Language Models (LLM) have brought significant performance increase on many Natural Language Processing tasks. However LLMs have not been tested for meeting summarization. This research paper examines the effectiveness of the gpt-3.5-turbo model on the meeting summarization domain. However due to input length limitations, it cannot be applied directly to this task. Thus the paper investigates two segmentation methods: a simple context-length window approach and topic segmentation using Latent Dirichlet Allocation (LDA). The context-length window approach's performance is close to the Pointer Generator framework. The topic segmentation gives worse results. Overall gpt-3.5-turbo performs worse with both approaches than state-of-the-art models which use a transformer architecture adapted for long documents.

This paper presents a novel approach to detect agreement and disagreement moments between participants in meeting transcripts without relying on labeled data. We propose a model in which disagreement detection is defined as the process of first identifying argumentative theses relevant to a given corpus of text and then classifying all phrases in the text as being either in favor of, against or expressing no opinion on a given thesis. To identify relevant theses, we compare the performance of a latent Dirichlet allocation-based topic model against that of a diverse set of large language models. To classify the stance of a phrase with respect to a thesis, only large language models are used. We find that, while state-of-the-art large language models do not outperform topic modeling-based approaches in extracting semantically relevant content, they are capable of presenting such content in a more concise and grammatically correct manner. We also find that state-of-the-art large language models are not capable of accurately performing stance classification as described above.

Behavior support applications require an understanding of the user's values to provide personalized support. User models have been developed to capture this relationship, but they may not always address unforeseen circumstances due to the changing nature of human values. To address this, the ability to update value models in real-time is crucial. This paper presents a study on the design of a graphical user interface (GUI) that utilizes behavior trees to elicit and update personal values. The study focuses on comparing the accuracy and usability of different questioning techniques, specifically comparative questions and isolation questions, and user interfaces. The results show that GUI with isolation questions and text user interface with comparative questions performed better in terms of accuracy compared to GUI with comparative questions. The study also highlights the possible impact of discrepancies in experimental procedures on the results. Furthermore, the limitations of the behavior tree model and the need for model improvements are discussed. Future work is proposed to address these limitations and explore enhancements such as alternative models, supervised processes for baseline tree building, frictionless elicitation techniques, consideration of random value shifts and big life events, and the potential of large language models. Overall, the findings contribute to the understanding of eliciting and updating user value models in personalized systems.

Since the recent rise and advancement of video conferencing platforms such as Zoom, it has become important to interpret the logistics of remote online meetings. Analysing verbal and non-verbal cues (such as body language) between members of these virtual forums can provide additional information regarding the level of conversational involvement of each party. This research focuses on age, gender, demographics and virtual background differences in the context of group conversational discussions. It argues that groups formed of younger adults have a higher level of involvement compared to the older groups. Similarly, this study found that groups with a higher ratio of male participants score better in virtual conversational engagement compared to women preponderant groups. To better understand the influence of these inter- and intra-personal characteristics, a corpus formed of 45 online meetings on the topic of Covid-19 was used. This set of data consists of questionnaires with measurements (demographics and other personal values), as well as detailed annotations of conversational signals, which provide valuable insights into the research topic of conversational involvement.

This study includes an experiment to investigate the involvement of individual backgrounds in the prediction of group conversational engagement. Four predictive models were used, namely the Decision Tree, Random Forest, Linear Regression and Generalized Linear Mixed Effects Models. While the Generalized Linear Mixed Effects Model provides more meaningful observations on the statistical effect of these factors, the Random Forest ultimately proved to give the best performance accuracy. The purpose of this research is to improve the connection between humans and technology by studying how inter- and intra- characteristics of individuals impact the involvement of a group in virtual interaction.

Involvement in an online setting has become increasingly more important in a rapidly digitizing world. Previous research has suggested that lexical alignment is an effective method for increasing interpersonal involvement. Multi-modal systems exist for analysing involvement, but the lexical alignment modality is not commonly used in these. This study examines the lexical alignment modality as a predictive measure of involvement in online group meetings. By analyzing participation indicators in virtual meetings, the aim is to identify moments of high involvement. Results indicate that lexical alignment does not correlate with moments of high involvement. The findings contribute to the scientific understanding of leveraging the verbal modality for analyzing and organizing online meetings.

Behavior support applications aim to provide personalized and flexible support to users in various domains. To achieve this, understanding users' preferences, values, and context is crucial. Creating user models that incorporate users' norms and values has been proposed as a solution to capture the relationship between desired behaviors and values. However, updating and modifying user models at run-time remains a challenge, as users' norms and values may change over time. This study investigates the accuracy of an audio interface designed to elicit values-related information using isolated questions. This involves designing an audio interface and evaluating its effectiveness through participant interactions, where they are presented with four scenarios. It was found that the audio interface performs above average in terms of usability, as indicated by the System Usability Scale score. The accuracy of the user models is evaluated through the Hamming distance and value differences between the base model and the participant-improved model. Most models required a small number of changes, and when changes were made, they were generally minimal. Additionally, feedback collected through open-ended interview questions lays down a basis for further development. The study contributes to the field by demonstrating the efficacy of the audio interface and its potential for updating user models in real-time. Overall, the research findings support the development of more effective and personalized behavior support applications that can adapt to its users.

With the rise in popularity of online conferencing platforms such as Zoom and Teams, there is an increasing need to understand the dynamics of virtual meetings to enhance productivity. One key aspect that can contribute to productive online meetings is conversational involvement. Personality traits influence the behaviour of participants within a group and therefore, have the potential to influence conversational involvement. This research paper aims to explore the impact of personality traits on conversational involvement in online meetings. To interpret personalities, the HEXACO model has been employed. Through a systematic investigation, this study provides valuable insights into how individual personalities influence engagement levels in a group during virtual meetings. The results of this study shed light on the relationship between personality traits and conversational involvement, thus contributing to the existing body of knowledge surrounding effective online meeting practices.

The alignment of behavior support systems with our personal values becomes increasingly important as behavior support systems continue to influence our daily lives. The purpose of this paper was to explore the use of graphical interfaces and isolation questions to elicit personal values and build accurate user models. An experiment was conducted in two phases to assess the accuracy and usability of the created interface. A comparison was also made with other types of interfaces developed within the research group. This experiment provided valuable insights but also held some limitations. The findings form a valuable contribution for future research and development in building responsible AI and personalized assistance from behavior support agents.

Negotiation Support Systems (NSSs) can provide help based on the preference setting (domain, issue weights, issue ranking, strategies, etc.) of the users of the systems. However, sometimes the users of the systems might make mistakes in the preference setting. With wrong preferences, the NSSs might provide suggestions that conflict with the users' desires. This thesis focuses on designing a mechanism that could detect the mismatches in issue weights of the users of NSSs and provide recommendations on updating the issue weights with a scoring method. This mechanism also has the ability to provide different recommendations based on the users' self-confidence level. A case study is done with six simulation experiments to test the mechanism. The results show that during the negotiation, the users of the NSS (PN) can have a chance to change the issue weights when mismatches occurred and the NSS (PN) can provide better suggestions after the issue weights are updated. The limitations of the mechanism are concluded at the end together with the future thoughts

This research set out to find whether the use of gestures by virtual embodied agents could affect perceptions of integrity by humans. 48 participants played a quiz with the assistance of a gesturing and an idle agent. Likert scales were used to evaluate their perceptions of integrity, which showed no significant difference between either agent. Interestingly, the actions of the participants showed something different: participants chose the gesturing agent to provide assistance a significantly higher number of times than the idle agent. One possible explanation for this result is that even though participants did not consciously notice a difference between the two agents, they subconsciously did while performing the quiz task. Another explanation is that choosing the gesturing agent more did not have to do with perceived integrity, but rather perceived ability, benevolence, or something else entirely.

Optimization of traffic signal control has been widely investigated by means of model-based strategies. In 2012 a new model-based controller was published, named Schedule-driven Intersection Control (SCHIC). This controller uses a job-scheduling algorithm to minimize the cumulative delay for all observed vehicles. The algorithms of SCHIC are at the basis of the commercially deployed controller SURTRAC. This means that SCHIC is partly used in real-world traffic situations and its algorithms are well documented. Therefore, it was possible to reproduce this controller in this thesis. Previous literature often evaluated SCHIC in simplified scenarios with (1) unrealistically large look-ahead times for vehicle detection. Secondly, (2) in most scenarios all approaching vehicles were restricted to request a single turning-movement at the intersection thereby removing inter-phase uncertainty (as explained directly below). Inter-phase uncertainty occurs when the chosen entry-lane of a vehicle determines the green phase that it requires from the traffic light controller. So, inter-phase uncertainty occurs under the following two conditions. Condition 1 – an approaching vehicle must eventually choose between different entry-lanes, with each entry-lane being associated to a different turning-movement at the intersection. Condition 2 – each of these individual entry-lanes is serviced during a different and competing green phase. For example, inter-phase uncertainty occurs when two competing green phases service either the straight-ahead or the left-turning entry-lane. The aim of this thesis is to evaluate SCHIC with both (1) realistic look-ahead times and (2) inter-phase uncertainty, since these two uncertainties are often not found in previous literature, however, they are commonly found in reality. Thus, it is not directly clear how the reported results would translate to more realistic settings, which include these two common uncertainties. In this study SCHIC is re-implemented and evaluated on a typical Dutch urban intersection located in The Hague. The lane dimensions of this intersection determine the look-ahead times as used in this thesis. For this study, a new method is introduced to reduce inter-phase uncertainty which is inspired by lane change prediction modules for automated driving. This method, Lane Change Prediction (LCP), makes use of e.g. radar-equipment in order to observe: (1) the driving style of each approaching vehicle and (2) the upcoming traffic state on each reachable entry-lane. Based on these two observations a classification model, i.e. a Random Forest, predicts the desired entry-lane before the vehicle actually enters it. This thesis researches whether the potentially higher accuracy of LCP will improve SCHICs performance as compared to its simpler statistical baselines, fixed-label and split-vehicles, which could have a lower accuracy. All traffic scenarios are simulated with Simulation of Urban Mobility (SUMO). Two popular driver-models are evaluated which determine the dynamic behaviour of the vehicles in simulation, i.e. the Intelligent Driver Model (IDM) and Krauss driver-model. The results showed that despite its higher accuracy, LCP does not significantly improve SCHICs performance over its baseline methods fixed-label and split-vehicles. Furthermore, the maximum theoretical performance gain of using LCP is evaluated with perfect predictions (Oracle). When applying perfect predictions, the average speed of all vehicles increased by 0.65% and 0.51% for the Intelligent Driver Model (IDM) and Krauss driver-model, respectively. The average number of stops per vehicle was reduced by 1.53% and 1.12% for the IDM and Krauss driver-model, respectively. Overall, it is concluded that the performance potential of all methods, i.e. the {Oracle, split-vehicles, fixed-label and LCP}, is quite comparable on SCHICs traffic flow. These methods show some performance potential, however, their confidence intervals are relatively wide which draws them closer to each other. In our scenario, the intersection in The Hague, another method might increase SCHICs traffic flow more significantly.

University students are expected to study on their own for large amounts of time. However a lot of these hours are not spent effectively by students. Eventually students who have trouble with self-studying in an effective manner may end up failing courses because of this. When students realise they have a (self-study) problem they can seek aid by contacting the academic counsellor. However, there are problems with this workflow: students often need time to acknowledge they are having issues. Then, even when students contact the academic counsellor to get help, it is a difficult task to provide personalised support for each student.
In this thesis we investigate the feasibility of a self-study support agent that can assist students with feedback on their self-study behavior. This agent can support students next to the academic counsellors. An agent can continuously track students and provide immediate feedback, whereas counsellors have a limited amount of time available (per student). As part of this thesis we conduct a focus group with academic counsellors, organise a workshop with first year students to create a design for a prototype self-study support agent. Thereafter we implement this prototype and use this in an experiment where the activities of several first year students are tracked over the period of two weeks. Then we analyse the data collected with our prototype agent. In doing so we show that the concept of the self-study support agent is feasible. We envision that future work can realise actual deployment of a self-study support agent.

Negotiation is the art of resolving conflicts of interests by finding outcomes that all parties agree with. Humans negotiate often, as there are many conflicts of interest in everyday life when working with other humans. Computer scientists have build computer agents that are capable of negotiating with each other in an attempt to outperform or replace humans in negotiation processes. These software-based agents are based on a negotiation strategy that is designed by a researcher. However, as for humans, creating an optimal negotiation strategy is difficult and sticking to the same strategy regardless of the negotiation setting is a bad idea (e.g. cultural differences). In this thesis, we automate the process of strategy configuration for automated negotiation agents to improve negotiation strategies with less human effort. In contrary to past attempts on this topic, the scale of the problem size and the applicability are vastly increased. We also automated the creation of multiple complementary strategies to exploit differences between negotiation settings and pick successful counter strategies. We show that we are capable of finding better strategies through automation and that switching between strategies based on the setting improves negotiation pay-off.