The process of design argumentation is primarily guided by determining the critical information and identifying any overlooked data. The goal is to navigate this uncertain terrain and enhance decision-making by underlining potential unknowns and biases.

Designers consistently encounter a multifaceted environment that challenges them to use more than just problem-solving and analytical skills. Creating effective solutions requires the identification, framing, and iterative redefinition of challenges. The complexity usually stems from the uncertainty about the significance of particular information and the inability to detect unknown factors timely.

Research consisting of 14 initial interviews was conducted under the premise that AI could significantly simplify the design process by reducing its complexity. The interviews revealed that there are well-established methods for summarizing and clarifying information, primarily through divergent and convergent strategies.

Complexity emerges when there is insufficient knowledge about the subject. The initial phase involves extensive questioning among stakeholders to eliminate these unknowns. Gathering a primary set of ideas, hypotheses, and potential issues significantly addresses this challenge.

Designers frequently find it challenging to dissect problems, mainly when time constraints restrict research. Users may sometimes adapt to specific issues, compounding the difficulty in identifying problems. A systemic view of problems is optimal, but the inherent complexity often makes this approach impractical.

The process of decision-making is a crucial aspect that requires significant attention. Making decisions iteratively and transparently is critical. For this purpose, designers can effectively condense issues and create clear presentations for stakeholders.

There are inherent problems in using AI in design. Biased human data could lead to undesirable results, which makes the propensity to prioritize the most probable outcomes detrimental to design.

The large language models are founded on data that tends to retain our societal thought patterns, implying that AI alone cannot solve problems that need a shift in thought structures. Complicating the matter is the existence of self-reinforcing loops, further solidifying societal thought patterns and influencing actions and expectations that drive our communities.

The training data of AI represent human perceptions and interpretations, as explained in "Preferable Futures" by Nohr and Kaldrack. They note that "data-driven AI exposes the prejudices and wishful thinking of those who feed it, thus stabilizing social structures and expectations. AI has been used in various contexts to limit uncertainties, whether in decision-making systems, training simulations, or full enterprise simulations. These applications share a common goal: controlling or making contingency controllable, leading to a rationality of "predictability.""

In business and economics, simulations designed to reduce uncertainties already impact our society's operations. They go beyond just scientifically predicting the future, instead suggesting, directing, manipulating, and creating futures premised on a belief in continuity, thereby stabilizing trajectories and path dependencies.

The use of AI and large language models in design processes should be mindful of society's aspirations for progress and innovation. Existing beliefs, whether embodied in AI or in humans, should not impede our ability to envision and work towards preferable futures. This requires a conscious shift away from relying solely on the most probable outcomes projected by AI.

The demand for a more comprehensive societal transformation, as necessitated by crises like climate change and species extinction, requires rethinking these profoundly impacting technologies. AI struggles to address challenges that require a paradigm shift, mainly because it operates within and potentially accelerates the established thought structures of our society.

To summarize these complex matters more simply, there is a quote by Rolf F. Nohr: "Or to put it more bluntly: the (uncertain) future imploded into a kind of "feedback-effected present" in which tendencies are intensified or subdued. The future was hedged and immobilized". The fundamental idea is that we have no absolute independence of action since our decisions are based on the present thought structures.

The self-reinforcing nature of design decisions, mainly how they are shaped by societal thought structures, can be very dangerous. Understanding these cycles is critical, especially when addressing systemic challenges such as climate change or social inequality. This necessitates reviewing the underlying thought processes guiding design decisions for innovation.

It can be beneficial for designers to be critically reflective and recognize how their choices contribute to these feedback loops. Additionally, they may understand the broader context and implications of their decisions. This involves questioning the assumptions, norms, values, and perceptions that underpin thought structures. Design professionals must embrace a more holistic mindset, considering the immediate implications of design choices and how these decisions will resonate across time and societies.

AI has the potential to enhance idea generation in design by critically questioning the thought structures behind concrete ideas without influencing the ideas themself. Its extensive and differentiated data can help draw the designer's attention to new aspects of the process.

By challenging current thought patterns, we can break cycles and create space for preferable changes not constrained by existing thought structures.

The intention behind this is to identify gaps in the design argument that can arise due to the mental models, assumptions, and biases of the designers. More specifically, the application should support the discovery of 'unknown unknowns,' which the designer inherently cannot recognize alone.

The responsible use of AI should be ensured by not generating decisions or ideas but by providing stimuli for reflection that go beyond what the designer could consider unaugmented. This augmentation allows for a broadening perspective, enabling designers to challenge their inherent biases and assumptions. Reflection and questioning interrupt the manifested self-reinforcing feedback loops by exposing and questioning underlying assumptions before decision-making.

The focus is to reduce the complexity of the design process, not by filtering out valuable information but by enabling designers to manage uncertainty better, enhancing their capacity to transition from existing situations to preferred ones.