This case study seeks to examine what would occur if we stripped away the defining features of our classification system and opted to push an object through a series of strict physical-only checks to truly determine if an object is or isn't a chair.
Given the ease with which we as humans classify objects as chairs, I believe that this study will expose the fragility of human classification when confined to a rigid exclusion-based logic system.
The algorithm operates by passing an object through a series of nine physical-only criteria checks that function as logic gates, rejecting objects that do not fit the criteria and passing those that do to the next branch.
Clarke argues that information is not only understood through its definition but also through a process of differentiation.
Clarke's theory shaped my algorithmic design, serving as a primary influence behind restricting my algorithm to exclusive systematic exclusion to eliminate what a chair is not, rather than a confirmation algorithm to determine what a chair is.
Sterne argues against the idea of verisimilitude—the idea that when attempting to reduce something to a binary system, part of the original object is lost. This loss reduces complexity and adds noise to the loop.
This algorithm argues that Sterne is incorrect in his analysis of verisimilitude and that when attempting to remove (compress) the inherently human components of exclusion, the algorithm introduced noise into the loop, which was seen through edge cases that slipped through the system.
"Standard" crushes the traditional representation of measurement by removing all markings from a standard meter stick to illustrate the underlying systems of knowledge that influence our interactions with the world around us, structured around a shared definition.
Her work reveals another key failure point of the algorithm, seen when attempting to classify an object as a chair after the final branch. My algorithm, without the human features of context, knowledge, and experience, was unable to establish an accepted value and rendered it impossible to classify any object that made it through all branches definitively as a chair.
Results of the case study indicated that there is a paradoxical relationship between the approach and the inherent reliance on human-like intuition for an exclusion-based approach to classification.
Although objects such as toilets passed all nine checks, some objects that humans may have classified as chairs were successfully rejected by the algorithm. This was one of the bright spots of the algorithm, as it not only succeeded in filtering, however solidified the theory that functionality is one of the crucial components to the human-classification system.
Although the algorithm was successful in filtering these objects out, other objects passed through all nine checks and would not be considered chairs. These objects were chair-like in nature, such as toilets, electric mobility scooters, and couches. The failure of the algorithm to distinguish a toilet from a chair arose due to the specific human-like knowledge, experience, and context that made the distinctions possible.
This failure was directly due to the loss of human processing in the loop and revealed that without this foundation, the algorithm cannot truly establish if an object is or is not a chair.
Run the Algorithm