This thesis investigates whether large language models (LLMs) produce consistent and neutral outputs when the same prompts are given in English and Arabic. It begins by reviewing technological, philosophical, psychological, and linguistic factors that can influence the behavior of the multilingual model. Consistency is defined as stability in content and tone, while neutrality refers to the absence of biased or emotionally loaded framing.

Ten prompts (seven sensitive and three non-sensitive) were refined through an iterative English ablation process and then translated into Arabic. Six leading LLMs were queried in both languages, and their outputs were analyzed using automated sentiment analysis to measure differences in emotional tone. In parallel, a survey of bilingual English and Arabic speakers evaluated model responses on sentiment consistency, factual consistency, and perceived neutrality in each language, along with the neutral framing of the prompts.

Results indicate that non-sensitive prompts are rated as less neutral but exhibit fewer inconsistencies in sentiment and factuality across English and Arabic outputs. In contrast, sensitive prompts are perceived as more neutral overall but exhibit larger differences in both sentiment and factual alignment. Among the models tested, some demonstrate higher consistency across languages than others. Automated analysis shows English outputs often carry more positive or mixed tones, while Arabic outputs lean toward neutrality. Human evaluations mirror these patterns for non-sensitive topics but differ for the more politically charged prompts, highlighting that automated tools do not align well with human perception in sensitive contexts.

These findings underscore the importance of combining automated metrics with human judgment to assess multilingual reliability and neutrality. The study suggests that improving balance in training data, improving transparency about language-specific behaviors, and guiding users to anticipate multilingual variations are key to developing fairer and more reliable GenAI systems.

Non-intrusive load monitoring (NILM) is a well-researched concept that aims to provide insights into individual appliance energy usage without the need for dedicated meters. This paper explores the possibility of applying the NILM concept to disaggregate energy data from a community level to a household level. By doing so, it addresses privacy concerns associated with real-time household data collection through smart meters. The proposed approach leverages sequence-to-point learning, a deep learning method, to perform the disaggregation. A method that showed promising results in the domain of NILM. The results show good performance on the evaluation metrics, but they also show that the model does a poor job of capturing the load signature.

Electrical load forecasting, namely short-term load forecasting, is essential to power grids’ safe and efficient operations. The need for accurate short-term load forecasting becomes increasingly pressing with increased renewable energy sources, which are stochastic in their power supply. Most forecasting models are focused on the temporal information for predictions, ignoring the spatial information of neighbouring houses. However, as neighbouring houses are often under similar circumstances, such as weather and holiday conditions, predictions
could benefit from this information. Moreover, aggregating electric loads could further improve the accuracy of predictions, as the loads become less
stochastic when aggregated. This paper looks at Graph WaveNet, which can capture the hidden spatial dependencies of different houses without needing geographic information about the houses. The framework is compared against a baseline on different aggregation levels. The results show that the framework can benefit from aggregating the residential electrical loads and improves over the baseline on all aggregation levels.

The ever-evolving power grid is becoming smarter and smarter. Modern houses come with smart meters and energy conscious consumers will buy additional smart meters to place in their home to help monitor their energy consumption. This new smart technology also opens the door to more accurate power consumption forecasting. In this study we look at utilizing a partial hierarchy, in which one of the appliances in a household is modelled separately from the rest of the house, to help improve household energy consumption forecasting accuracy. This is done in conjunction with Auto Regressive Moving Average (ARMA) based models. Three variants of ARMA based models will be looked at: Auto Regressive Integrated Moving Average (ARIMA), Seasonal Auto Regressive Integrated Moving Average (SARIMA), and Auto Regressive Integrated Moving Average with Exogenous variables (ARIMAX). These methods will then be compared to more baseline approaches such as a persistence method and a seasonal moving average. Our analysis has led us to conclude that the partial hierarchy model offers little to no benefit when applied in the field of household energy consumption forecasting when built upon ARMA based models. ARMA based models in general appeared to be poor performers when it came to household energy consumption forecasting.

Bottom up load forecasting, is a technique where energy consumption forecasts are made on lower spatial levels, after which the resulting forecasts are aggregated to form forecasts of higher spatial levels. With the current move to renewable energy sources and the importance of reducing the strain on an already congested electricity grid, accurately forecasting both the location and time of future energy consumption has become more important than ever. To this end, this paper analyses the impact of applying bottom up load forecasting to different spatial levels on the electricity grid, including appliance, household, community and city spatial levels. Energy consumption data for these spatial levels were gathered from the 15-minute Texas and California datasets from Pecan Street Dataport. The results obtained in this study, suggest that energy consumption volatility at lower spacial levels and forecast difficulty at higher spacial levels, play an important role in the performance of applying the bottom up load forecasting technique to energy consumption forecasting problems.

Non-Intrusive Load Monitoring (NILM) is a technique used to disaggregate household power consumption data into individual appliance components without the need for dedicated meters for each appliance. This paper focuses on improving the generalizability of NILM algorithms to unseen households using Convolutional Neural Networks (CNNs) and one-shot transfer learning. The research investigates the effectiveness of one-shot transfer learning in fine-tuning a CNN model to accurately detect the ON/OFF state of appliances in households not seen during the training phase of the CNN. The study utilizes the Pecan Street dataset for training and evaluation, which includes detailed energy consumption records from various locations in the United States. The results suggest that one-shot transfer learning could enhance the performance of the NILM algorithm, particularly when multiple data samples are used for fine-tuning. However, the effectiveness of one-shot transfer learning varies strongly depending on the number of samples and the characteristics of the target household.

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

The field of automated negotiation promises to improve negotiations, thus, a fair outcome and process should also be considered when building these systems. However, issues exist with computational approaches to fairness with which the field of computer science is mainly concerned. To this end, we propose a new approach to fairness based on that of essentially contested concepts to see if argumentation-based negotiation could be used as an extension to the Stacked Alternating Offers Protocol to improve fairness. Looking at fairness as an essentially contested concept shows that discussion between people somehow influenced by the negotiation system is necessary to maintain its fairness. This in turn means that systems that provide accessible context are fairer than systems that would not do so. Thus arguments, if implemented in an accessible manner, add more context to the negotiation, in turn making an SAOP negotiation fairer.

Is there a way to incorporate fairness in the opponent modeling component of an automated agent? Since opponent modeling plays an important role in a negotiation strategy, it is reasonable to research how fairness can be integrated into this component, as it influences the outcome of the negotiation. A first step towards finding an answer to this question is to define fairness and how this definition can be translated to algorithmic fairness. The next step is to investigate already available opponent models and assess whether their strategy can be considered fair or not. This paper analyses the process of Bayesian learning in the context of opponent modeling, and tries to reveal possible flaws or strengths that the model has embedded in it, with the hope to add relevant information in the area of automated negotiation.

As automated negotiating agents become more and more part of our daily life, additional care needs to be taken that the agents can negotiate fairly. Humans each have their own intrinsic view on fairness, which affects the negotiation processes and the degree to which the outcome is viewed as satisfactory. However, most current agents are built around a specific notion of fairness, which could be potentially unwanted in certain scenarios or by some individuals. This paper presents a generic framework through which varying fairness notions can be implemented. The performance of the framework is tested through a selection of proposed agents with different fairness views. The results show that the agents are able to reach an agreement on the pareto frontier that is fair given their notion. Thus they do not sacrifice optimality for fairness.

In this paper, the unintended consequences, also named edge cases in this paper, of integrating fairness into the automated negotiation process are researched. By finding these unintended consequences, we can deal with them accordingly or avoid them, as to not cause any problems with our fairness metric that might make our negotiation process less fair or cause any undesired behaviour. Edge cases are searched for in a small-scale experiment by implementing the difference principle from the 'Justice as Fairness' notion by John Rawls into the negotiation process. The negotiation has two agents, and the behaviour of one of the agents is changed to adhere to the difference principle. By using these agents in automated negotiations with different domains, the behaviour and outcomes of these negotiations between two agents will be checked for any abnormalities that could be considered an unintended consequence. From this, we conclude that agent implementing fairness has a smaller available bidding space which leads to a staler negotiation process. Furthermore, the outcomes show that not always an optimal result is found. However, no unintended consequences directly related to fairness were found. Since finding edge cases is an exhaustive process, which can be compared to finding bugs in a computer program, the research done is not proof that the Rawlsian notion of fairness, or any kind of fairness for that matter, has no other unintended consequences. The research in this paper can be used as inspiration for further research into the edge cases of fairness in automated negotiation, and to have a general idea as to what unintended consequences Rawlsian fairness brings.

This paper aims to define the broad concept of fairness and investigate how it can be measured, especially considering fairness in automated negotiations. The report relies on the work on fairness issues that have been derived from the research of C. Albin [1]. Firstly, the paper elaborates on different fairness metrics from the literature review. Then these metrics are tested to assess if they capture the effect of considering fairness by the agents in the negotiations. That is done by simulating multiple bilateral negotiations under the open-source GeniusWeb framework, where eccentric agents are compared with fairness-oriented parties by using the fairness metrics. Based on the conducted experiment, the most consistent fairness metric in automated negotiations is the distance to the game-theoretic solutions to the bargaining problem, which considers much of the outcome fairness concept. However, other investigated metrics also capture the different scope of fairness and can be used as metrics, especially when combined, and some interdependence between metrics is modelled.