Mycket av plånbokens innehåll och funktion, såsom medlemskort och betallösningar, är idag ersatt av mobila lösningar. Däremot finns det i Sverige ingen mobil lösning för id-kort som kan användas på samma sätt som det fysiska id-kortet. Denna uppsats syftar till att undersöka huruvida svenska myndigheter ser ett behov av en mobilanpassad id-kortslösning och, om detta behov konstateras, föreslå en modell för hur en sådan lösning skulle kunna utformas. För att undersöka detta ämne har tre intervjuer med svenska myndigheter (Elegitimationsnämnden, Polismyndigheten och Transportstyrelsen) samt en litteraturstudie genomförts. Uppsatsens resultat består av tre kunskapsprodukter: en behovsanalys, en kravsammanställning och en infrastrukturmodell. Behovsanalysen undersöker behovet av en mobilanpassad id-kortslösning, sett från de intervjuade myndigheternas perspektiv. Denna analys visar att myndigheterna ser ett behov av en sådan lösning. Kravsammanställningen består av krav på en mobilanpassad id-kortslösning som härletts från de genomförda intervjuerna samt från uppsatsens huvudsakliga teoribas: Kim Camerons The Laws of Identity (2005). Infrastrukturmodellen är utformad efter dessa krav och beskriver hur en mobilanpassad id-kortslösning skulle kunna utformas för att passa in i den svenska infrastrukturen för digital identitetshantering. Modellen är även utformad för att följa Hanseth och Lyytinens (2010) designregler för informationsinfrastrukturutveckling och den visar att utveckling av en mobilanpassad id-kortslösning i Sverige är tekniskt genomförbar. / Much of the contents and functionality of the traditional wallet, such as membership cards and payment solutions, have been replaced by smartphone solutions. Yet, in Sweden there is no smartphone based solution for ID cards that can be used in the same way as the physical ID card. This paper examines whether the Swedish authorities see a need for a smartphone based ID card solution and, if such a need is identified, suggests a model for how such a solution could be designed. To explore this topic, a literature review and three interviews with Swedish authorities (the E-identification Board, the Police Authority and the Transport Agency) have been conducted. The results of this report consists of three knowledge products: a demand analysis, a requirements specification and an infrastructure model. The demand analysis examines the need for a smartphone based ID card solution, as seen from the interviewed authorities' perspective. This analysis indicates that the authorities see a need for such a solution. The requirements specification consists of requirements for a smartphone based ID card solution, derived from the conducted interviews and Kim Cameron's The Laws of Identity (2005). The infrastructure model is designed to meet these requirements and describes how a smartphone based ID card solution could be designed to fit into the Swedish infrastructure for digital identity management. The model is also designed to follow the design rules for information infrastructure development derived by Hanseth and Lyytinen (2010) and shows that the development of a smartphone based ID card solution in Sweden is technically feasible.

In this research, three aspects of data quality with regard to artifical intelligence (AI) have been investigated: detection of misleading fake data, especially deepfakes, data scarcity, and data insufficiency, especially how much training data is required for an AI application. Different application domains where the selected aspects pose issues have been chosen. To address the issues of data privacy, security, and regulation, these solutions are targeted for edge devices. In Chapter 3, two solutions have been proposed that aim to preempt such misleading deepfake videos and images on social media. These solutions are deployable at edge devices. In Chapter 4, a deepfake resilient digital ID system has been described. Another data quality aspect, data scarcity, has been addressed in Chapter 5. One of such agricultural problems is estimating crop damage due to natural disasters. Data insufficiency is another aspect of data quality. The amount of data required to achieve acceptable accuracy in a machine learning (ML) model has been studied in Chapter 6. As the data scarcity problem is studied in the agriculture domain, a similar scenario—plant disease detection and damage estimation—has been chosen for this verification. This research aims to provide ML or deep learning (DL)-based methods to solve several data quality-related issues in different application domains and achieve high accuracy. We hope that this work will contribute to research on the application of machine learning techniques in domains where data quality is a barrier to success.