Suchergebnisse

Verifiable credentials, coupled with decentralized ledger technologies, have been potential providers of trustworthy digital identity for individuals, organizations, and other entities, and thus, potential enablers of trustful digital interactions. The rapid development of this technology—called self-sovereign identity (SSI)—and the ecosystems built around it have been fostered even more by the societal needs stemming from the current pandemic crisis, when governments, non-profit organizations, businesses, and individuals are working together on different aspects of SSI to enable mainstream adoption. In this study, we build on rich qualitative data gathered from SSI practitioners to give a fresh overview of the perceived benefits and challenges of SSI. The paper advances research on the domain of SSI adoption and provides valuable insights into the feasibility of SSI for practitioners both in the private and public sectors ; peerReviewed

A welfare reform involving the mixing of social and health services is being introduced by the Finnish government. Several factors have been identified for this shift, the most relevant of which is that many information systems are not interoperable and data-related problems have been identified. Management and isolated offers of service. Digitalizing to solve these issues is part of the government's policy. This paper outlines the change landscape and explores the architectural options of the proposed digitalisation process and crucial success factors. The Undertaking The design is applied to the largest county, which consists of over 1,900 information systems related to social support and health care. The goal is to design a single joint framework with no more than 300 supporting information systems, resulting in savings of EUR 3 billion. Six key scientific conclusions are presented as the results. ; peerReviewed

The Finnish government has decided to implement a reform in the social and healthcare system by combining the two in the future. There are several drivers for this change that have been identified. Large number of information systems that are not interoperable, challenges in data management and isolated service offering are the most significant ones. In the government's strategy for the future the digitalisation will provide tools to solve these challenges. In this paper the landscape is outlined and the architecture choices are discussed. The enterprise architecture is applied for the largest county comprising of over 1900 social service and healthcare related information systems. The target is to design one joint system with maximum of 300 supporting information systems resulting in 3 B EUR savings. ; peerReviewed

Various recent Artificial Intelligence (AI) system failures, some of which have made the global headlines, have highlighted issues in these systems. These failures have resulted in calls for more ethical AI systems that better take into account their effects on various stakeholders. However, implementing AI ethics into practice is still an on-going challenge. High-level guidelines for doing so exist, devised by governments and private organizations alike, but lack practicality for developers. To address this issue, in this paper, we present a method for implementing AI ethics. The method, ECCOLA, has been iteratively developed using a cyclical action design research approach. The method aims at making the high-level AI ethics principles more practical, making it possible for developers to more easily implement them in practice. ; peerReviewed

From Efficiency to Effectiveness: Delivering Business Value Through Software -- The Rise of Software Startup Research: An Insider's View -- There's no Business like Software Business: Trends in Software Intensive Business Research -- A SECO meta-model - A Common Vocabulary of the SECO Research Domain -- Towards an Understanding of iIoT Ecosystem Evolution - MindSphere Case Study -- Identifying Architecture Attributes in the Context of Software Ecosystems Based on a Mapping Study -- Activities and Challenges in the Planning Phase of a Software Ecosystem -- API Management Challenges in Ecosystems -- The Product Roadmap Maturity Model DEEP: Validation of a Method for Assessing the Product Roadmap Capabilities of Organizations -- Towards a SaaS Pricing Cookbook: A Multi-vocal Literature Review -- Managing Commercial Conflicts of Interest in Open Source Foundations -- Dynamic Data Management for Machine Learning in Embedded Systems: A Case Study -- Fostering Continuous Innovation with Engaging IT-Assisted Transparent Information Sharing: A Case Study -- Change Management Practices for Continuous Delivery - A Systematic Literature Mapping -- Leveraging Business Transformation with Machine Learning Experiments -- Intertwined Development of Business Model and Product Functions for Mobile Applications: A Twin Peak Feature Modeling Approach -- The Role of Customer in an Agile Project: Amulti-case Study -- Cloud-Based Solution for Construction Documentation and Quality Management – Examination of the Value-in-Use -- Initial Coin Offering (ICO) as a Fundraising Strategy: A Multiple Case Study on Success Factors -- Enabling Circular Economy with Software: A Multi-level Approach to Benefits, Requirements and Barriers -- Implementing AI Ethics in Practice: An Empirical Evaluation of the RESOLVEDD-Strategy -- Towards a Better Society - A Analysis of the Value Basis of the European eGovernment and Data Economy -- Educational Innovations and Gamification for Fostering Training and Testing in Software Implementation Projects -- Improving a Startup Learning Framework Through an Expert Panel -- A Board Game to Teach Team Composition in Software Startups -- Does Self-efficacy Matter? On the Correlation of Self-efficacy and Creativity in IT Education -- Hard Competencies Satisfaction Levels for Software Engineers: A Unified framework -- How Software Startup Teams Reflect: Approaches, Triggers and Challenges -- Amidst Uncertainty -- or Not? Decision-Making in Early-Stage Software Startups -- Customer Churn Prediction in B2B Contexts -- Online Multiplayer Games for Crowdsourcing the Development of Digital Assets - The Case of Ingress -- Organizational Innovativeness Relies on Business and IT Alignment -- MVP Development Process for Software Startups -- Technical Debt Trade-off - Experiences from Software Startups Becoming Grownups -- A Dynamic Software Startup Competency Model -- Objectives and Challenges in Finnish Software Companies 2018 - Interview of 99 Finnish Software Development Firms -- The Impact of IT Bootcamp on Student Learning - Experience from ICT Enabled Experiential-Based Course -- Implementing Artificial Intelligence Ethics: A Tutorial

There appears to be a common agreement that ethical concerns are of high importance when it comes to systems equipped with some sort of Artificial Intelligence (AI). Demands for ethical AI are declared from all directions. As a response, in recent years, public bodies, governments, and universities have rushed in to provide a set of principles to be considered when AI based systems are designed and used. We have learned, however, that high-level principles do not turn easily into actionable advice for practitioners. Hence, also companies are publishing their own ethical guidelines to guide their AI development. This paper argues that AI software is still software and needs to be approached from the software development perspective. The software engineering paradigm has introduced maturity model thinking, which provides a roadmap for companies to improve their performance from the selected viewpoints known as the key capabilities. We want to voice out a call for action for the development of a maturity model for AI software. We wish to discuss whether the focus should be on AI ethics or, more broadly, the quality of an AI system, called a maturity model for the development of AI systems. ; peerReviewed

Ethical concerns related to Artificial Intelligence (AI) equipped systems are prompting demands for ethical AI from all directions. As a response, in recent years public bodies, governments, and companies have rushed to provide guidelines and principles for how AI-based systems are designed and used ethically. We have learned, however, that high-level principles and ethical guidelines cannot be easily converted into actionable advice for industrial organizations that develop AI-based information systems. Maturity models are commonly used in software and systems development companies as a roadmap for improving the performance. We argue that they could also be applied in the context of developing ethically aligned AI systems. In this paper, we propose a maturity model for AI ethics and explain how it can be devised by using a Design Science Research approach. ; peerReviewed