Traditionally, the AI field has focused on providing answers in domains like chess, translation, protein folding, and item recommendations. These AI systems have a clear objectives and outcomes that can be easily classified as correct or incorrect. However, once such systems face the complexity of social contexts, they start to produce unintended and sometimes harmful outcomes. If we are to design AI systems that avoid far-reaching harmful consequences, we cannot decouple/detach them from the complex systems in which they operate. A key challenge in doing so is AI systems' behaviour-use interdependence (i.e., the behaviour of a system is related to the manner, in which it is used) – a topic underrepresented in extant literature. We set out to explore this issue, guided by the central research question: "How can we design a theoretical model that facilitates early simulation of AI systems' behaviour-use interdependence using Design theories?”. This dissertation presents both theoretical and empirical investigations into the development of such a model. These lay the foundation for what we term the Theoretical Model for Prototyping AI or PAI model. The PAI model is defined by relationships among abduction, induction, and deduction, which offer a means to support the early simulation of the behaviour-use interdependence. Finally, the devising of the PAI model allows us to shed light into how Design theories could contribute to the design of better AI systems. It also allows us to extend these theories and identify potential future directions for the field of Design. @en

Artificial intelligence (AI) systems have been widely applied to various contexts, including high-stake decision processes in healthcare, banking, and judicial systems. Some developed AI models fail to offer a fair output for specific minority groups, sparking comprehensive discussions about AI fairness. We argue that the development of AI systems is marked by a central paradox: the less participation one stakeholder has within the AI system’s life cycle, the more influence they have over the way the system will function. This means that the impact on the fairness of the system is in the hands of those who are less impacted by it. However, most of the existing works ignore how different aspects of AI fairness are dynamically and adaptively affected by different stages of AI system development. To this end, we present a use case to discuss fairness in the development of corporate wellness programs using smart wearables and AI algorithms to analyze data. The four key stakeholders throughout this type of AI system development process are presented. These stakeholders are called service designer, algorithm designer, system deployer, and end-user. We identify three core aspects of AI fairness, namely, contextual fairness, model fairness, and device fairness. We propose a relative contribution of the four stakeholders to the three aspects of fairness. Furthermore, we propose the boundaries and interactions between the four roles, from which we make our conclusion about the possible unfairness in such an AI developing process.

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Interactions of users with AI powered solutions (AIPS) have the potential to affect collective behaviours and amplify unanticipated outcomes. Product developers, organisations, and companies are increasingly being expected to take responsibility for the unanticipated outcomes of their products. In this paper we explore a proactive approach to prototyping AIPS-user interactions using Social Virtual Reality (SVR) environments that allows for the anticipation of potential outcomes. We contend that doing so would limit the detrimental effect outcomes could have on product developers' resources and reputation.

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A fundamental shift in the way society operates is approaching driven by advances in the field of artificial intelligence (AI). Yet, there is a comparative lack of discourse across the design discipline regarding this topic. While there are fragments of methodological readiness for designing (with/for) AI, the nuances of such need to be further explored. The aim of this article is to shed light on these and suggest a possible way forward for design that can ensure AI-powered artifacts remain safe even as their utility evolves over time.
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