Context. Technical Debt (TD) refers to short-term beneficial software solutions that impede future changes, making TD management essential. However, establishing a TD management (TDM) process is one of the most pressing concerns in practice. Goal. We plan to identify which previously researched TDM approaches are feasible in practice and what typical challenges emerge to create a guideline for establishing a TDM process. Method. We replicated our previously published action research study by conducting five workshops introducing TDM with two teams from different companies. To determine the feasibility of TDM approaches, we presented the teams with approaches for various TD activities and let them decide which to adopt. Overall, we conducted 19 workshops and retrospectives, analyzing 108 meetings (96 hours) over a 30 -month period. Results. The adopted TD prevention strategies and documentation were similar in all teams. The teams utilized their respective backlogs and created a new backlog item type for TD, incorporating similar attributes such as interest, contagiousness, a resubmission date, and reminders to discuss drawbacks and risks. However, they used different prioritization approaches and deviating repayment methods. The teams had to overcome similar challenges during the establishment, which we list in this paper. Conclusions. We identified the TDM approaches used by all teams as a starting point for best practices. For challenges, we provided solutions or identified them as research gaps. Issue tracking system vendors should implement TD issue types employing the identified attributes. Finally, we created a white paper for practitioners to establish a TDM process based on our results.

In today's society, where Artificial Intelligence (AI) has gained a vital role, concerns regarding user's trust have garnered significant attention.The use of AI systems in high-risk domains have often led users to either under-trust it, potentially causing inadequate reliance or over-trust it, resulting in over-compliance.Therefore, users must maintain an appropriate level of trust.Past research has indicated that explanations provided by AI systems can enhance user understanding of when to trust or not trust the system.However, the utility of presentation of different explanations forms still remains to be explored especially in high-risk domains.Therefore, this study explores the impact of different explanation types (text, visual, and hybrid) and user expertise (retired police officers and lay users) on establishing appropriate trust in AI-based predictive policing.While we observed that the hybrid form of explanations increased the subjective trust in AI for expert users, it did not led to better decision-making.Furthermore, no form of explanations helped build appropriate trust.The findings of our study emphasize the importance of re-evaluating the use of explanations to build [appropriate] trust in AI based systems especially when the system's use is questionable.Finally, we synthesize potential challenges and policy recommendations based on our results to design for appropriate trust in high-risk based AI-based systems.

Urbanization has increased societal tensions and led to the growth of citizen participation in urban planning, which is often conducted in computer-supported environments and progressively online to include a high number of citizens. Past projects have shown that digital participation creates new challenges and that collaborative online discussions do not achieve the quality of on-site scenarios, as the interactions, in-depth exchange of opinions, and quality of contributions vary. Within a design science research project, we examine how to support citizens in creating comprehensible contributions on urban participation platforms. We identify issues, formulate meta-requirements, and derive design principles to implement feasible prototypes that we evaluated in focus groups. Our findings extend the existing research about urban participation platforms in civic tech, urban informatics, and planning, with validated design principles that specify AI-based feedback and further features to increase interactions and support citizens in producing more specific contributions.

What if artificial intelligence (AI) machines became teammates rather than tools? This paper reports on an international initiative by 65 collaboration scientists to develop a research agenda for exploring the potential risks and benefits of machines as teammates (MaT). They generated 819 research questions. A subteam of 12 converged them to a research agenda comprising three design areas – Machine artifact, Collaboration, and Institution – and 17 dualities – significant effects with the potential for benefit or harm. The MaT research agenda offers a structure and archetypal research questions to organize early thought and research in this new area of study.