Growing up, social constructs like roles, norms and values are being internalised and naturalised. Despite offering a sense of stability, such constructs also prohibit equality, justice and diversity, by pushing people into categories, roles and norms they do not represent. However, once internalised, social constructs fall under the surface of awareness, making their mitigation and re-framing a complex task. This also poses a great challenge for designers, who often aim to create fair and inclusive futures for those marginalised and discriminated against. Attempts are made to mitigate bias by introducing artificial intelligence (AI). Technology however, often acts as a double-edged sword, having the abilities to both identify and mitigate bias, or amplify inequality, reinforce existing stereotypes and increase injustice. Recognising both the potential but also the limitations of AI, this thesis explores the idea of reflexive designer-AI interactions, as a new form of human-machine collaboration towards more reflective design practitioners who are able to surface and dismantle and rethink personal and collective imaginings. Seeing a key role in reflection, often criticised behaviour of AI, like inconsistency, unpredictability and confrontation, are being explored as potentially meaningful for triggering critical and self-reflective thinking and decision making in design. Following a speculative and introspective research through design approach, this thesis explores such reflexive interactions in the context of gender representation in child toys. The hypothesis is that the introduction of reflection and a change in mindset when engaging with AI, can be productive in terms of mitigating gender bias in child toys. In order to envision the situated designer-AI interactions, a speculative vision of the first gender fluid child toy company is introduced. This vision serves as a tool for presenting queer future AI-design practices, but also as a critique of current gender stereotypes in the design of children's toys. Technological exploration insights are translated into four design tactics, resulting in designer reflection and bias awareness. Those tactics are applied and explored in practice, by designing three gender-ambiguous child toys. Each toy represents a new reflexive design-AI workflow. Each workflow differently illustrates human and non-human collaboration that surfaces, de-familiarizes and dismantles personal and collective imaginings of gender in toys. Additionally, these speculative practices also challenge the status quo in design, raising awareness about design and AI issues that need to be addressed further in the future. Taking into account the insights from the experiments, as well as prototype testing with children and evaluating expert interviews, this project concludes that reflexive interactions – as proposed alternative to traditional human-AI interactions and in addition to the current design practice – are potentially productive to surface, dismantle and re-familiarize personal bias and collective imaginings. Furthermore, does this thesis suggest that AI’s often negatively described behaviour like confusion and inconsistency, also carry the power to trigger reflective practices that help surfacing and challenge bias. However potentially limiting factors like ecological, economical and social cost as well as ethical concerns are discussed.

Creative conversation among designers and stakeholders in a design project enables new ideas to naturally originate and evolve. Language allows for the exchange of values, priorities, and past experience whilst keeping solution forms usefully ambiguous. Yet there is a danger that only the language of people directly involved in the design process gets to be heard, limiting how inclusively the problems are interpreted, which in turn can impede how complex design problems are addressed. Recent advances in artificial intelligence (AI) have shown the exclusionary spaces that are often inhabited by designers, engineers, and developers of new artefacts and technologies. On the other hand, text data used to train language models for machine learning applications have the potential to highlight societal biases in ways that designers can utilise. In this paper, we report the results of an exploratory study using AI text generation to synthesize and narrate opinions and experiences that may be unfamiliar to designers. Three pairs of designers were given a complex socio-technical problem to solve. Of these, two pairs interacted with an AI text generator during the task, while one pair acted as a baseline condition. Analysing the conversational exchanges between the designers and the designers & AI, we observe how the use of AI leads to prompting nuanced interpretations of problems and ideas, opening up the objective problem and design lenses and interpretations. Finally, we discuss how the designers (re)assign different roles to the AI to suit their creative purposes.@en