Corrupting data and sensing error, or how to 'see' digital images.

This article examines the implications of computational environments for digital image making, focusing on how glitches—momentary machine malfunctions—reveal systemic conditions beyond mere technical errors. It explores glitch aesthetics as both an artistic practice and a means to expose the underlying infrastructures of digital images, highlighting how these reveal embedded social discriminations and algorithmic biases rather than neutral technological failures. Through examples such as generative machine learning projects and glitch-based artworks, the article argues for moving beyond traditional visual epistemologies toward sensing digital images as affective phenomena entangled with social, cultural, and technical systems. This approach challenges conventional understandings of photography and image representation in the era of networked, data-driven computational culture.