The usage of automated decision making-systems by public institutions letting the system decide on the approval, determination or denial of individuals benefits as an example, is an effective measure in making more amount of work done in a shorter time period and to a lower cost than if it would have been done by humans. But still, although the technology has developed into being able to help us in this way, so has also the potential problems that these systems can cause while they are operating. The ones primarily affected here will be the individuals that are denied their benefits, health care, or pensions. The systems can maintain hidden, historical stigmatizations and prejudices, disproportionally affecting members of a certain historically marginalized group in a negative way through its decisions, simply because the systems have learned to do so. There is also a risk that the actual programmer includes her or his own bias, as well as incorrect translation of applicable legislations or policies causing the finalized system to make decisions on unknown bases, demanding more, less or completely other things than those requirements that are set up by the public and written laws. The language in which these systems works are in mathematical algorithms, which most ordinary individuals, public employees or courts will not understand. If suspecting that you could have been discriminated against by an automated decision, the requirements for successfully claim a violation of discrimination in US-, Canadian- and Swedish courts, ECtHR and ECJ demands you to show on which of your characteristics you were discriminated, and in comparison to which other group, a group that instead has been advantaged. Still, without any reasons or explanations to why the decision has been taken available for you as an applicant or for the court responsible, the inability to identify such comparator can lead to several cases of actual indirect discriminations being denied. A solution to this could be to follow the advice of Sophia Moreau’s theory, focusing on the actual harm that the individual claim to have suffered instead of on categorizing her or him due to certain traits, or on finding a suitable comparator. This is similar to a ruling of the Swedish Court of Appeal, where a comparator was not necessary in order to establish that the applicant had been indirectly discriminated by a public institution. Instead, the biggest focus in this case was on the harm that the applicant claimed to have suffered, and then on investigating whether this difference in treatment could be objectively justified. In order for Swedish and European legislation to be able to meet the challenges that can arise through the usage of automated decision making-systems, this model of the Swedish Court of Appeal could be a better suited model to help individuals being affected by an automated decision of a public institution, being potentially indirectly discriminative.

The thesis investigates the feasibility of automated decision-making (ADM) in project management from two perspectives - technical feasibility, analysed through a comprehensive literature review, and organisational acceptance, evaluated through empirical evidence. To address technical feasibility, the literature study is used, and it underscores the significance of data-driven decision-making and the impact of advancements in machine learning. Organisational acceptance is investigated with thematic analysis, and the complementary method employed is semi-structured interviews, allowing for in-depth insights from experienced project managers. The analysis reveals that project integration, cost, and risk management exhibit considerable potential for ADM integration, whereas schedule, resource, and procurement management demonstrate varying levels of applicability. In contrast, scope, quality, communication, and stakeholder management are deemed less feasible due to their complex nature and the critical involvement of skilful project managers. As a result, the study advocates a balanced approach to ADM implementation, combining automated capabilities with human expertise. Its contributions lie in the formalisation and categorisation of ADM applications, addressing the challenges, and providing valuable insights for practical ADM adoption in project management.

Redes probabilísticas são modelos muito versáteis, com aplicabilidade crescente em diversas áreas. Esses modelos são capazes de estruturar e mensurar a interação entre variáveis, permitindo que sejam realizados vários tipos de análises, desde diagnósticos de causas para algum fenômeno até previsões sobre algum evento, além de permitirem a construção de modelos de tomadas de decisões automatizadas. Neste trabalho são apresentadas as etapas para a construção dessas redes e alguns métodos usados para tal, dando maior ênfase para as chamadas redes bayesianas, uma subclasse de modelos de redes probabilísticas. A modelagem de uma rede bayesiana pode ser dividida em três etapas: seleção de variáveis, construção da estrutura da rede e estimação de probabilidades. A etapa de seleção de variáveis é usualmente feita com base nos conhecimentos subjetivos sobre o assunto estudado. A construção da estrutura pode ser realizada manualmente, levando em conta relações de causalidade entre as variáveis selecionadas, ou semi-automaticamente, através do uso de algoritmos. A última etapa, de estimação de probabilidades, pode ser feita seguindo duas abordagens principais: uma frequentista, em que os parâmetros são considerados fixos, e outra bayesiana, na qual os parâmetros são tratados como variáveis aleatórias. Além da teoria contida no trabalho, mostrando as relações entre a teoria de grafos e a construção probabilística das redes, também são apresentadas algumas aplicações desses modelos, dando destaque a problemas nas áreas de marketing e finanças. / Probabilistic networks are very versatile models, with growing applicability in many areas. These models are capable of structuring and measuring the interaction among variables, making possible various types of analyses, such as diagnoses of causes for a phenomenon and predictions about some event, besides allowing the construction of automated decision-making models. This work presents the necessary steps to construct those networks and methods used to doing so, emphasizing the so called Bayesian networks, a subclass of probabilistic networks. The Bayesian network modeling is divided in three steps: variables selection, structure learning and estimation of probabilities. The variables selection step is usually based on subjective knowledge about the studied topic. The structure learning can be performed manually, taking into account the causal relations among variables, or semi-automatically, through the use of algorithms. The last step, of probabilities estimation, can be treated following two main approaches: by the frequentist approach, where parameters are considered fixed, and by the Bayesian approach, in which parameters are treated as random variables. Besides the theory contained in this work, showing the relations between graph theory and the construction of probabilistic networks, applications of these models are presented, highlighting problems in marketing and finance.

Redes probabilísticas são modelos muito versáteis, com aplicabilidade crescente em diversas áreas. Esses modelos são capazes de estruturar e mensurar a interação entre variáveis, permitindo que sejam realizados vários tipos de análises, desde diagnósticos de causas para algum fenômeno até previsões sobre algum evento, além de permitirem a construção de modelos de tomadas de decisões automatizadas. Neste trabalho são apresentadas as etapas para a construção dessas redes e alguns métodos usados para tal, dando maior ênfase para as chamadas redes bayesianas, uma subclasse de modelos de redes probabilísticas. A modelagem de uma rede bayesiana pode ser dividida em três etapas: seleção de variáveis, construção da estrutura da rede e estimação de probabilidades. A etapa de seleção de variáveis é usualmente feita com base nos conhecimentos subjetivos sobre o assunto estudado. A construção da estrutura pode ser realizada manualmente, levando em conta relações de causalidade entre as variáveis selecionadas, ou semi-automaticamente, através do uso de algoritmos. A última etapa, de estimação de probabilidades, pode ser feita seguindo duas abordagens principais: uma frequentista, em que os parâmetros são considerados fixos, e outra bayesiana, na qual os parâmetros são tratados como variáveis aleatórias. Além da teoria contida no trabalho, mostrando as relações entre a teoria de grafos e a construção probabilística das redes, também são apresentadas algumas aplicações desses modelos, dando destaque a problemas nas áreas de marketing e finanças. / Probabilistic networks are very versatile models, with growing applicability in many areas. These models are capable of structuring and measuring the interaction among variables, making possible various types of analyses, such as diagnoses of causes for a phenomenon and predictions about some event, besides allowing the construction of automated decision-making models. This work presents the necessary steps to construct those networks and methods used to doing so, emphasizing the so called Bayesian networks, a subclass of probabilistic networks. The Bayesian network modeling is divided in three steps: variables selection, structure learning and estimation of probabilities. The variables selection step is usually based on subjective knowledge about the studied topic. The structure learning can be performed manually, taking into account the causal relations among variables, or semi-automatically, through the use of algorithms. The last step, of probabilities estimation, can be treated following two main approaches: by the frequentist approach, where parameters are considered fixed, and by the Bayesian approach, in which parameters are treated as random variables. Besides the theory contained in this work, showing the relations between graph theory and the construction of probabilistic networks, applications of these models are presented, highlighting problems in marketing and finance.

Workplace studies in Human-Computer Interaction (HCI) is a research field that has expanded in an explosive way during the recent years. Today there is a wide range of theoretical approaches and methods to choose from, which makes it problematic to make methodological choices both in research and system design. While there have been several studies that assess the different approaches to workplace studies, there seems to be a lack of studies that explore the theoretical and methodological differences between more structured methods within the research field. In this thesis, a comparative theoretical and empirical analysis of three methods for workplace studies is being conducted to deal with the following research problem: What level of theoretical depth and methodological structure is appropriate when conducting methods for workplace studies to inform design of complex socio-technical systems? When using the two criterions descriptive power and application power, to assess Contextual Design (CD), Determining Information Flow Breakdown (DIB), and Capturing Semi-Automated Decision-Making (CASADEMA), important lessons are learned about which methods are acceptable and useful when the purpose is to inform system design.

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The aim of this thesis is to gain knowledge of how automated decision-making in Swedish public authorities affects the ability to comply with the Swedish principle of public access to official records. This is done through a Posthumanist analysis of seven qualitative interviews. With the help of the actor-network theory concept of the nonhuman actor the underlying computer programs of automated decision-making is understood as an actor who, in interaction with human actors, affects the public sector´s ability to provide the public with valuable information about how they run their organizations. The interviews were conducted with employees at two Swedish public organizations: The Swedish Board of Student Finance and Swedish Association of Local Authorities and Regions. The respondents were asked what kind of insight in the underlying computer programs for automated decision-making that they themselves and the public thought was needed. The interviews were recorded and transcribed, and provided the material for the analysis. The study shows that the employees share the opinion that insight in the automated decision-making systems are not necessary for the public. As a consequence of this, the system developers are left with exclusive knowledge of how the public sector is making decisions and the decision-making becomes invisible for a majority of the public. This is not in line with the aim of the principle of public access to official records. This is a two years master’s thesis in Archival Science.