• The study explores the experiences of gender of N =93 development professionals working in the Finnish Information Technology sector • Results indicated that women are significantly more aware of gender-based cognitive bias in IT design and development • Women reported significantly higher familiarity with Design Thinking methodology, although actual usage rates were similar across genders • Men were found to engage in user studies to mitigate cognitive bias • Professionals over 51 years of age were more likely to utilize discussion as a means to address gender-based cognitive bias Cognitive bias is a concern in artificial intelligence (AI) development. Research shows the prominence of cognitive bias within algorithms. We argue that cognitive bias is more than training data, but rather development team composition. Design Thinking (DT) is an approach used to reduce bias via multidisciplinary expertise. The article presents a study examining DT in addressing gender-based cognitive bias in the Finnish information technology industry. The aim was to examine how the gender of IT professionals influences familiarity with and use of DT, coupled with awareness and addressing of cognitive bias in IT development processes. A mixed method questionnaire was used to collect data from N=93 participants. Questions probed familiarity with DT, use of DT, and cognitive bias handling in participants’ organizations. Non-parametric tests were used to analyze quantitative data, due to abnormal distributions. Atlas.ti was used to code and analyze the qualitative data. Categorization determined whether participants recognized bias in their work, and the importance they attributed towards dealing with gender-based bias in IT. Women were more likely to view gender-based cognitive bias as relevant. Women were significantly more familiar with DT as a methodology (p = 0.028), men were significantly more likely to engage in user studies (p = 0.018). Older participants showed a tendency to emphasize the importance of open discussion more than other participant groups, with some analyses indicating a trend-level difference (p = 0.085). Qualitative responses indicated the importance of discussion in development teams to avoid or mitigate bias, suggesting the need for organizational psychological safety. The paper provides novel contributions to the human dimension of bias in AI and IT in general. Results show that men and women IT professionals were aware of DT, yet men professionals were more likely to mitigate bias through collecting insight from end-users.

This paper explores the dynamics of Human-Robot Interaction (HRI) in public spaces, focusing on how humanoid robots engage with human crowds in the competitive RoboCup Soccer environment. We examine the role of spectatorship, where emotional engagement arises through indirect observation of engineering-driven competition, drawing parallels between human soccer and robot sports. The potential for autonomous systems to elicit collective emotions and systematically study such experiences is investigated. Using the Autonomy Levels for Unmanned Systems (ALFUS) framework, we assess RoboCup soccer robots' autonomy in terms of mission complexity (MC), environmental complexity (EC), and external system independence (ESI). Additionally, the Autonomy and Technology Readiness Assessment (ATRA) method supports gradual capability enhancement, providing a roadmap to higher autonomy. Based on this established methodology, we introduce the Robot-Crowd Interaction Framework (R-CIF), a novel conceptual framework defining the roles of actors involved, to connect theoretical insights with real-world applications. This work highlights the significance of crowd affectivity in robotic sports to boost public engagement and proposes directions for future research on collective emotional dynamics in HRI.

Geert Hofstede famously labelled culture as the “software of the mind”, affecting how people cognitively process the world, and how organisations, communities and societies are structured. This lends to explain how culture influences the ways that people, perceive, use and experience technology design, and how within user experience design, cultural logic should be applied to develop user interfaces (UI). This study draws on Hofstede’s cultural dimension of ‘uncertainty avoidance’ (UA) to examine how UA, or the ways in which people within certain cultures cope with uncertainty, unknown and change, to examine the influence of culture on self-service technology (STT) UI design. The authors evaluate a sample of ten UIs from various STTs in Japan, a country of higher UA (N = 5), and Finland (N = 5) a country of lower UA. The results show that in higher UA cultures design of STT’s UI often rely on multimodal interaction, bright colours, and clear progress guidance via illustrations. However, we find also some contradictions in design solutions within the same cultures. It seems that instead of designers’ cultural identities playing a role, designers’ expertise in usability, company brand, and requirements by context affect how UI components are constructed. We discuss theoretical impacts of these manifestations of UI design on how they relate to accessibility and usability. As an implication to the practice, we propose a UI design assumption that embraces ‘Zero Uncertainty’, combining clear flow guidance, text and illustrations, with multimodal guidance and feedback.

Ethics of Artificial Intelligence (AI) is a growing research field that has emerged in response to the challenges related to AI. Transparency poses a key challenge for implementing AI ethics in practice. One solution to transparency issues is AI systems that can explain their decisions. Explainable AI (XAI) refers to AI systems that are interpretable or understandable to humans. The research fields of AI ethics and XAI lack a common framework and conceptualization. There is no clarity of the field’s depth and versatility. A systematic approach to understanding the corpus is needed. A systematic review offers an opportunity to detect research gaps and focus points. This article presents the results of a systematic mapping study (SMS) of the research field of the Ethics of AI. The focus is on understanding the role of XAI and how the topic has been studied empirically. An SMS is a tool for performing a repeatable and continuable literature search. This article contributes to the research field with a Systematic Map that visualizes what, how, when, and why XAI has been studied empirically in the field of AI ethics. The mapping reveals research gaps in the area. Empirical contributions are drawn from the analysis. The contributions are reflected on in regards to theoretical and practical implications. As the scope of the SMS is a broader research area of AI ethics, the collected dataset opens possibilities to continue the mapping process in other directions.

Abstract Artificial Intelligence (AI) systems are increasing in significance within software services. Unfortunately, these systems are not flawless. Their faults, failures and other systemic issues have emphasized the urgency for consideration of ethical standards and practices in AI engineering. Despite the growing number of studies in AI ethics, comparatively little attention has been placed on how ethical issues can be mitigated in software engineering (SE) practice. Currently understanding is lacking regarding the provision of useful tools that can help companies transform high-level ethical guidelines for AI ethics into the actual workflow of developers. In this paper, we explore the idea of using user stories to transform abstract ethical requirements into tangible outcomes in Agile software development. We tested this idea by studying master’s level student projects (15 teams) developing web applications for a real industrial client over the course of five iterations. These projects resulted in 250+ user stories that were analyzed for the purposes of this paper. The teams were divided into two groups: half of the teams worked using the ECCOLA method for AI ethics in SE, while the other half, a control group, was used to compare the effectiveness of ECCOLA. Both teams were tasked with writing user stories to formulate customer needs into system requirements. Based on the data, we discuss the effectiveness of ECCOLA, and Primary Empirical Contributions (PECs) from formulating ethical user stories in Agile development.

Usability is a key element in successful software. Ensuring the technical usability of a learning solution enables users to focus on their main task, learning. The purpose of this paper is to demonstrate the results of heuristic usability evaluations of digital learning solutions. Heuristic evaluations were conducted on 24 digital learning solutions from one country (Finland) and two country groups (Asian countries and Spanish speaking countries) concentrating on the usability of the user interface of each evaluated solution. The main results of this study indicate that a few heuristics cover the majority of all usability problems (UPs) observed in learning solutions, but these heuristics contain a relatively low proportion of the UPs rated as severe. The results also indicated differences in the usability problems (UPs) observed between different types of digital learning solutions and between digital learning solutions from different countries or country groups.

Global conditions are changing at such a rate that foreseeing trends in technological development, economic fluctuations and climatic conditions is ever more difficult. When developing technologies, there is one constant factor that practitioners and researchers should be aware of, and that is people. This is not to say that people, culture and social conditions remain stagnant, for these too evolve with the surrounding circumstances. Rather, appropriate tools and capabilities for investigating people, their lives and life situations, are integral to understanding what people need in terms of technology, how these technologies will be used, and more importantly how they will be valued in the scheme of a person's life. This paper describes the process and outcomes of a course in Cognitive Science focused on developing the tools needed for Life-Based Service Design (LBSD). The course is implemented via problem-based learning (PBL), and students are guided through the process by charting an explanatory method adhering to the Life-Based Design (LBD) ontology. This ontology comprises: 1) Form-of-life analysis; 2) service concept and requirements; 3) fit-for-life analysis; and 4) innovation design. Results show heightened awareness and sensitivity of life conditions, values and needs, revealing design concept strengths and weaknesses in the pre-development phase.