“Glitch Identity” **will engage with audiences by scanning their faces, yet assigning a deliberately randomized identity, such as ‘A dinosaur that likes to dance.’
How do machines label us and shape our identity with biased categorization? In an era where Generative AI astonishes with its capabilities, it also possesses the propensity for misrecognition due to its reliance on algorithmic classification. My project, “Glitch Identity,” seeks to reveal and critique how machine learning algorithms may categorize human and real-world diversity into oversimplified and stereotyped groups.
Prototype of Glitch Identity
Although AI is impressive, deriving its understanding from massive datasets, it is not immune to the biases embedded categorically within its algorithmic framework (Crawford and Paglen). The fact is, the real world is not as simple as a distinct category that AI perceives. Reality frequently defies these rigid classifications with its continuous and non-linear attributes. To address this limitation, “Glitch Identity” **will engage with audiences by scanning their faces, yet assigning a deliberately randomized identity, such as ‘A dinosaur that likes to dance,’ to underscore the absurdity and inaccuracies intrinsic to these algorithms.
