Controlling antennas’ vertical tilt through Remote Electrical Tilt (RET) is an effective method to optimize network performance. Reinforcement Learning (RL) algorithms such as Deep Reinforcement Learning (DRL) have been shown to be successful for RET optimization. One issue with DRL is that DRL models have a black box nature where it is difficult to ’explain’ the decisions made in a human-understandable way. Explanations of a model’s decisions are beneficial for a user not only to understand but also to intervene and modify the RL model. In this work, a state-ofthe-art Explainable Reinforcement Learning (XRL) method is evaluated on the RET optimization problem. More specifically, the chosen XRL method is the Embedded Self-Prediction (ESP) model proposed by Lin, Lam, and Fern [16] which can generate contrastive explanations in terms of why an action is preferred over the other. The ESP model was evaluated on two different RET optimization scenarios. The first scenario is formulated as a single agent RL problem in a ’simple’ environment whereas the second scenario is formulated as a multi agent RL problem with a more complex environment. In both scenarios, the results show little to no difference in performance compared to a baseline Deep Q-Network (DQN) algorithm. Finally, the explanations of the model were validated by comparing them to action outcomes. The conclusions of this work is that the ESP model offers explanations of its behaviour with no performance decrease compared to a baseline DQN and the generated explanations offer value in debugging and understanding the given problem. / Att styra antenners vertikala lutning genom RET är en effektiv metod för att optimera nätverksprestanda. RL-algoritmer som DRL har visat sig vara framgångsrika för REToptimering. Ett problem med DRL är att DRL-modeller är som en svart låda där det är svårt att ’förklara’ de beslut som fattas på ett sätt som är begripligt för människor. Förklaringar av en modells beslut är fördelaktiga för en användare inte bara för att förstå utan också för att ingripa och modifiera RL-modellen. I detta arbete utvärderas en toppmodern XRL-metod på RET-optimeringsproblemet. Mer specifikt är den valda XRL-metoden ESP-modellen som föreslagits av Lin, Lam och Fern [16] som kan generera kontrastiva förklaringar i termer av varför en handling föredras framför den andra. ESP-modellen utvärderades på två olika RET-optimeringsscenarier. Det första scenariot är formulerat som ett problem med en enstaka agent i en ’enkel’ miljö medan det andra scenariot är formulerat som ett problem med flera agenter i en mer komplex miljö. I båda scenarierna visar resultaten liten eller ingen skillnad i prestanda jämfört med en DQN-algoritm. Slutligen validerades modellens förklaringar genom att jämföra dem med handlingsresultat. Slutsatserna av detta arbete är att ESPmodellen erbjuder förklaringar av dess beteende utan prestandaminskning jämfört med en DQN och de genererade förklaringarna ger värde för att felsöka och förstå det givna problemet.

Deep Learning models are ubiquitous in machine learning. They offer state-of- the-art performance on tasks ranging from natural language processing to image classification. The drawback of these complex models is their black box nature. It is difficult for the end-user to understand how a model arrives at its prediction from the input. This is especially pertinent in domains such as medicine, where being able to trust a model is paramount. In this thesis, ways of explaining a model predicting paroxysmal atrial fibrillation from sinus electrocardiogram (ECG) data are explored. Building on the concept of feature attributions, the problem is approached from three distinct perspectives: time, scale, and frequency. Specifically, one method based on the Integrated Gradients framework and one method based on Shapley values are used. By perturbing the data, retraining the model, and evaluating the retrained model on the perturbed data, the degree of correspondence between the attributions and the meaningful information in the data is evaluated. Results indicate that the attributions in scale and frequency are somewhat consistent with the meaningful information in the data, while the attributions in time are not. The conclusion drawn from the results is that the task of predicting atrial fibrillation for the model in question becomes easier as the level of scale is increased slightly, and that high-frequency information is either not meaningful for the task of predicting atrial fibrillation, or that if it is, the model is unable to learn from it. / Djupinlärningsmodeller förekommer på många håll inom maskininlärning. De erbjuder bästa möjliga prestanda i olika domäner såsom datorlingvistik och bildklassificering. Nackdelen med dessa komplexa modeller är deras “svart låda”-egenskaper. Det är svårt för användaren att förstå hur en modell kommer fram till sin prediktion utifrån indatan. Detta är särskilt relevant i domäner såsom sjukvård, där tillit till modellen är avgörande. I denna uppsats utforskas sätt att förklara en modell som predikterar paroxysmalt förmaksflimmer från elektrokardiogram (EKG) som uppvisar normal sinusrytm. Med utgångspunkt i feature attribution (särdragsattribution) angrips problemet från tre olika perspektiv: tid, skala och frekvens. I synnerhet används en metod baserad på Integrated Gradients och en metod baserad på Shapley-värden. Genom att perturbera datan, träna om modellen, och utvärdera den omtränader modellen på den perturberade datan utvärderas graden av överensstämmelse mellan attributionerna och den meningsfulla informationen i datan. Resultaten visar att attributioner i skala- och frekvensdomänerna delvis stämmer överens med den meningsfulla informationen i datan, medan attributionerna i tidsdomänen inte gör det. Slutsatsen som dras utifrån resultaten är att uppgiften att prediktera förmaksflimmer blir enklare när skalnivån ökas något, samt att högre frekvenser antingen inte är betydelsefullt för att prediktera förmaksflimmer, eller att om det är det, så saknar modellen förmågan att lära sig detta.

AI has quickly grown from being a vast concept to an emerging technology that many companies are looking to integrate into their businesses, generally considered an ongoing “revolution” transforming science and society altogether. Researchers and organizations agree that AI and the recent rapid developments in machine learning carry huge potential benefits. At the same time, there is an increasing worry that ethical challenges are not being addressed in the design and implementation of AI systems. As a result, AI has sparked a debate about what principles and values should guide its development and use. However, there is a lack of consensus about what values and principles should guide the development, as well as what practical tools should be used to translate such principles into practice. Although researchers, organizations and authorities have proposed tools and strategies for working with ethical AI within organizations, there is a lack of a holistic perspective, tying together the tools and strategies proposed in ethical, technical and organizational discourses. The thesis aims to contribute with knowledge to bridge this gap by addressing the following purpose: to explore and present the different tools and methods companies and organizations should have in order to build machine learning applications in a fair and transparent manner. The study is of qualitative nature and data collection was conducted through a literature review and interviews with subject matter experts. In our findings, we present a number of tools and methods to increase fairness and transparency. Our findings also show that companies should work with a combination of tools and methods, both outside and inside the development process, as well as in different stages of the machine learning development process. Tools used outside the development process, such as ethical guidelines, appointed roles, workshops and trainings, have positive effects on alignment, engagement and knowledge while providing valuable opportunities for improvement. Furthermore, the findings suggest that it is crucial to translate high-level values into low-level requirements that are measurable and can be evaluated against. We propose a number of pre-model, in-model and post-model techniques that companies can and should implement in each other to increase fairness and transparency in their machine learning systems. / AI har snabbt vuxit från att vara ett vagt koncept till en ny teknik som många företag vill eller är i färd med att implementera. Forskare och organisationer är överens om att AI och utvecklingen inom maskininlärning har enorma potentiella fördelar. Samtidigt finns det en ökande oro för att utformningen och implementeringen av AI-system inte tar de etiska riskerna i beaktning. Detta har triggat en debatt kring vilka principer och värderingar som bör vägleda AI i dess utveckling och användning. Det saknas enighet kring vilka värderingar och principer som bör vägleda AI-utvecklingen, men också kring vilka praktiska verktyg som skall användas för att implementera dessa principer i praktiken. Trots att forskare, organisationer och myndigheter har föreslagit verktyg och strategier för att arbeta med etiskt AI inom organisationer, saknas ett helhetsperspektiv som binder samman de verktyg och strategier som föreslås i etiska, tekniska och organisatoriska diskurser. Rapporten syftar till överbrygga detta gap med följande syfte: att utforska och presentera olika verktyg och metoder som företag och organisationer bör ha för att bygga maskininlärningsapplikationer på ett rättvist och transparent sätt. Studien är av kvalitativ karaktär och datainsamlingen genomfördes genom en litteraturstudie och intervjuer med ämnesexperter från forskning och näringsliv. I våra resultat presenteras ett antal verktyg och metoder för att öka rättvisa och transparens i maskininlärningssystem. Våra resultat visar också att företag bör arbeta med en kombination av verktyg och metoder, både utanför och inuti utvecklingsprocessen men också i olika stadier i utvecklingsprocessen. Verktyg utanför utvecklingsprocessen så som etiska riktlinjer, utsedda roller, workshops och utbildningar har positiva effekter på engagemang och kunskap samtidigt som de ger värdefulla möjligheter till förbättringar. Dessutom indikerar resultaten att det är kritiskt att principer på hög nivå översätts till mätbara kravspecifikationer. Vi föreslår ett antal verktyg i pre-model, in-model och post-model som företag och organisationer kan implementera för att öka rättvisa och transparens i sina maskininlärningssystem.

<p dir="ltr">In the dynamic realm of Artificial Intelligence (AI), this study explores the multifaceted landscape of Trustworthy AI with a dedicated focus on achieving both explainability and acceptance. The research addresses the evolving dynamics of AI, emphasizing the essential role of human involvement in shaping its trajectory.</p><p dir="ltr">A primary contribution of this work is the introduction of a novel "Trustworthy Explainability Acceptance Metric", tailored for the evaluation of AI-based systems by field experts. Grounded in a versatile distance acceptance approach, this metric provides a reliable measure of acceptance value. Practical applications of this metric are illustrated, particularly in a critical domain like medical diagnostics. Another significant contribution is the proposal of a trust-based security framework for 5G social networks. This framework enhances security and reliability by incorporating community insights and leveraging trust mechanisms, presenting a valuable advancement in social network security.</p><p dir="ltr">The study also introduces an artificial conscience-control module model, innovating with the concept of "Artificial Feeling." This model is designed to enhance AI system adaptability based on user preferences, ensuring controllability, safety, reliability, and trustworthiness in AI decision-making. This innovation contributes to fostering increased societal acceptance of AI technologies. Additionally, the research conducts a comprehensive survey of foundational requirements for establishing trustworthiness in AI. Emphasizing fairness, accountability, privacy, acceptance, and verification/validation, this survey lays the groundwork for understanding and addressing ethical considerations in AI applications. The study concludes with exploring quantum alternatives, offering fresh perspectives on algorithmic approaches in trustworthy AI systems. This exploration broadens the horizons of AI research, pushing the boundaries of traditional algorithms.</p><p dir="ltr">In summary, this work significantly contributes to the discourse on Trustworthy AI, ensuring both explainability and acceptance in the intricate interplay between humans and AI systems. Through its diverse contributions, the research offers valuable insights and practical frameworks for the responsible and ethical deployment of AI in various applications.</p>

Brain tumors represent a diverse spectrum of cancer types which can induce grave complications and lead to poor life expectancy. Amongst the various brain tumor types, gliomas are primary brain tumors that compose about 30% of adult brain tumors. They are graded according to the World Health Organization into Grades 1 to 4 (G1-G4), where G4 is the highest grade with the highest malignancy and poor prognosis. Early diagnosis and classification of brain tumor grade is very important since it can improve the treatment procedure and (potentially) prolong a patient's life, since life expectancy largely depends on the level of malignancy and the tumor's histological characteristics. While clinicians have diagnostic tools they use as a gold standard, such as biopsies these are either invasive or costly. A widely used example of a non-invasive technique is magnetic resonance imaging, due to its ability to produce images with different soft-tissue contrast and high spatial resolution thanks to multiple imaging sequences. However, the examination of such images can be overwhelming for radiologists due to the overall large amount of data. Deep learning approaches, on the other hand, have shown great potential in brain tumor diagnosis and can assist radiologists in the decision-making process. In this thesis, brain tumor grade classification in MR images is performed using deep learning. Two popular pre-trained CNN models (VGG-19, ResNet50) were employed using single MR modalities and combinations of them to classify gliomas into three grades. All models were trained using data augmentation on 2D images from the TCGA dataset, which consisted of 3D volumes from 142 anonymized patients. The models were evaluated based on accuracy, precision, recall, F1-score, AUC score, as well as the Wilcoxon Signed-Rank test to establish if one classifier was statistically significantly better than the other. Since deep learning models are typically 'black box' models and can be difficult to interpret by non-experts, Gradient-weighted Class Activation Mapping (Grad-CAM) was used in order to address model explainability. For single modalities, VGG-19 displayed the highest performance with a test accuracy of 77.86%, whilst for combinations of two and three modalities T1ce, FLAIR and T2, T1ce, FLAIR were the best performing ones for VGG-19 with a test accuracy of 74.48%, 75.78%, respectively. Statistical comparisons indicated that for single MR modalities and combinations of two MR modalities, there was not a statistically significant difference between the two classifiers, whilst for combination of three modalities, one model was better than the other. However, given the small size of the test population, these comparisons have low statistical power. The use of Grad-CAM for model explainability indicated that ResNet50 was able to localize the tumor region better than VGG-19.

The growing presence of algorithm-generated recommendations in AI-powered services highlights the importance of responsible systems that explain outputs in a human-understandable form, especially in an automotive context. Implementing explainability in recommendations of AI-powered eco-driving is important in ensuring that drivers understand the underlying reasoning behind the recommendations. Previous literature on explainable AI (XAI) has been primarily technological-centered, and only a few studies involve the end-user perspective. There is a lack of knowledge of drivers' needs and requirements for explainability in an AI-powered eco-driving context. This study addresses the attributes that make a “satisfactory” explanation, i,e., a satisfactory interface between humans and AI. This study uses scenario-based interviews to understand the explainability attributes that influence truck drivers' intention to use eco-driving recommendations. The study used thematic analysis to categorize seven attributes into context-dependent (Format, Completeness, Accuracy, Timeliness, Communication) and generic (Reliability, Feedback loop) categories. The study contributes context-dependent attributes along three design dimensions: Presentational, Content-related, and Temporal aspects of explainability. The findings of this study present an empirical foundation into end-users' explainability needs and provide valuable insights for UX and system designers in eliciting end-user requirements.

With an increase in complexity of software, developers rely more on reuse and dependencies in their source code via code snippets. As a result, it is becoming harder to identify and mitigate vulnerabilities. Although traditional analysis tools are still utilized, machine learning models are being adopted to expand efforts and combat such threats. Given the possibilities towards usage of such models, research in this area has introduced various approaches which vary in usability and prediction. In generalizing models to a more natural language approach, researchers have opted to train models on source code to identify existing and potential vulnerabilities. Exploratory research has been performed by treating source code as plain text, creating “text-based” models. With a motivation to prevent vulnerable code snippets, we present a dissertation on the effectiveness of text-based machine learning models for vulnerability detection. We utilize datasets composed of open-source projects and vulnerability types to generate our own training and testing data via extracted function pairings. Using this data, we evaluate a series of text-based machine learning models, coupled with natural language processing (NLP) techniques and our own data processing methods. Through empirical research, we demonstrate the effectiveness of such models based on statistical evidence. From these results, we determine negative correlations and identify "cross-cutting" features. Finally, we present analysis of models with "cross-cutting" feature removal to improve performance while providing explainability towards model decisions.

The field of wireless communications is arguably one of the most rapidly developing technological fields. Therefore, with each new advancement in this field, the complexity of wireless systems can grow significantly. This phenomenon is most visible in mobile communications, where the current 5G and 6G radio access networks (RANs) have reached unprecedented complexity levels to satisfy diverse increasing demands. In such increasingly complex environments, managing resources is becoming more and more challenging. Thus, experts employed performant artificial intelligence (AI) techniques to aid radio resource management (RRM) decisions. However, these AI techniques are often difficult to understand by humans, and may receive unimportant inputs which unnecessarily increase their complexity. In this work, we propose an explainability pipeline meant to be used for increasing humans’ understanding of AI models for RRM, as well as for reducing the complexity of these models, without loss of performance. To achieve this, the pipeline generates diverse feature importance explanations of the models with the help of three explainable artificial intelligence (XAI) methods: Kernel SHAP, CERTIFAI, and Anchors, and performs an importance-based feature selection using one of three different strategies. In the case of Anchors, we formulate and utilize a new way of computing feature importance scores, since no current publication in the XAI literature suggests a way to do this. Finally, we applied the proposed pipeline to a reinforcement learning (RL)- based RRM system. Our results show that we could reduce the complexity of the RL model between ∼ 27.5% and ∼ 62.5% according to different metrics, without loss of performance. Moreover, we showed that the explanations produced by our pipeline can be used to answer some of the most common XAI questions about our RL model, thus increasing its understandability. Lastly, we achieved an unprecedented result showing that our RL agent could be completely replaced with Anchors rules when taking RRM decisions, without a significant loss of performance, but with a considerable gain in understandability. / Området trådlös kommunikation är ett av de snabbast utvecklande tekniska områdena, och varje framsteg riskerar att medföra en signifikant ökning av komplexiteten för trådlösa nätverk. Det här fenomenet är som tydligast i mobil kommunikaiton, framför allt inom 5G och 6G radioaccessnätvärk (RANs) som har nåt nivåer av komplexitet som saknar motstycke. Detta för att uppfylla de ökande kraven som ställs på systemet. I dessa komplexa system blir resurshantering ett ökande problem, därför används nu artificiell intelligens (AI) allt mer för att ta beslut om hantering av radioresurser (RRM). Dessa AI tekniker är dock ofta svåra att förstå för människor, och kan således ges oviktig input vilket leder till att öka AI modellernas komplexitet. I detta arbete föreslås en förklarande pipeline vars mål är att användas för att öka människors förståelse av AI modeller för RRM. Målet är även att minska modellernas komplexitet, utan att förlora prestanda. För att åstadkomma detta genererar pipelinen förklaringar av betydande predikatorer för modellen med hjälp av tre metoder för förklarande artificiell intelligens (XAI). Dessa tre metoder är, Kernel SHAP, CERTIFAI och Anchors. Sedan görs ett predikatorurval baserat på predikatorbetydelse med en av dessa tre metoder. För metoden Anchors formuleras ett nytt sätt att beräkna betydelsen hos predikatorer, eftersom tidigare forskning inte föreslår någon metod för detta. Slutligen appliceras den föreslagna pipelinen på en förstärkt inlärnings- (RL) baserat RRM system. Resultaten visar att komplexiteten av RL modellen kunde reduceras med mellan ∼ 27, 5% och ∼ 62, 5% baserat på olika nyckeltal:er, utan att förlora någon prestanda. Utöver detta visades även att förklaringarna som producerats kan användas för att svara på de vanligaste XAI frågoran om RL modellen, och på det viset har även förståelsen för modellen ökat. Sistnämnt uppnåddes enastående resultat som visade att RL modellen helt kunde ersättas med regler producerade av Anchor-metoden för beslut inom RRM, utan någon störra förlust av prestanda, men med an stor vinst i förståelse.

[pt] À medida que os modelos de aprendizado de máquina penetram áreas críticas como medicina, sistema de justiça criminal e mercados financeiros, sua opacidade, que impede que as pessoas interpretem a maioria deles, se tornou um problema a ser resolvido. Neste trabalho, apresentamos uma nova taxonomia para classificar qualquer método, abordagem ou estratégia para lidar com o problema da interpretabilidade de modelos de aprendizado de máquina. A taxonomia proposta que preenche uma lacuna existente nas estruturas de taxonomia atuais em relação à percepção subjetiva de diferentes intérpretes sobre um mesmo modelo. Para avaliar a taxonomia proposta, classificamos as contribuições de artigos científicos relevantes da área. / [en] As machine learning models penetrate critical areas like medicine, the criminal justice system, and financial markets, their opacity, which hampers humans ability to interpret most of them, has become a problem to be solved. In this work, we present a new taxonomy to classify any method, approach or strategy to deal with the problem of interpretability of machine learning models. The proposed taxonomy fills a gap in the current taxonomy frameworks regarding the subjective perception of different interpreters about the same model. To evaluate the proposed taxonomy, we have classified the contributions of some relevant scientific articles in the area.