Locked in the Present
When used in the design process, AI consistently delivers the most probable outcomes, using its ability to analyze and predict on vast amounts of data. Using human-created data, the large language models are based on current thought structures and thus project the current beliefs into the future.
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.