Design Tech  Specialization  : 
 Ontography Diagram

Human Identity, Subjectivity, Self / Self-Sense, Body / Embodiment, Personhood, Visibility / Invisibility, Fragmentation, Ownership, Otherness, Authenticity, Dataset, Label / Annotation, Token, Category, Class, Feature, Attribute, Bird’s-eye Label, Visual Fragment, Statistical Category, Machine-Readable, Metadata, Bias, Consent, Collection / Data Collection Method, Pattern Recognition, Prediction, Algorithm, Deep Learning, Model, Training, Inference, Representation Learning, Generalization, Visual Culture, Interface, Perception, Physicalization, Material System, Sensor, Screen, Glitch / Distortion, Visibility / Invisibility, Representation, Ethics, Consent, Power, Surveillance, Privacy, Identity Politics, Categorization & Exclusion, Inequality, Normativity, Agency, Ecosystem, Network,

Project Question

We want to explore how human beings are increasingly abstracted and deconstructed into compressible data structures within large-scale machine learning systems. In today’s digital landscape, our bodies and identities are no longer seen as whole, subjective beings. Instead, they are reduced by the internet and AI into visual fragments, labeled tokens, statistical categories. For example, a face in a dataset is no longer tied to its owner — it becomes Asian / female / smiling / tilted gaze — a depersonalized unit optimized for pattern recognition and prediction.

This line of thinking raises the following questions:

    What happens to our sense of self when human subjectivity is converted into machine-readable fragments?
    How does annotation and labeling
    shape what is visible — and what becomes     invisible or lost?

    Can we design an     experiential interface that reveals this transformation and exposes what usually remains hidden?

Forking Paths  – possible project diagram

CRT stacks, circuit reliquaries, labeled faces, medical imaging, glitch strata

The map on the right forks into Software and Hardware trunks: TouchDesigner, facial-landmark detection, style transformation route signals into Raspberry Pi with LED matrices / old television screens, speakers, cameras, IR sensors and a circuit assemblage.

Pipeline: sensor data → feature extraction → generative AI → physical output, culminating in a video wall and a circuits-based sculpture. This diagram is the navigation key for future prototypes.
Moodboard
Moodboard
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Forking Path

Reflections on barriers to entry

1. Technical Barriers
As an installation, the project requires a large physical setup with circuits, cameras, sensors, displays. The challenge lies in balancing aesthetics with engineering:
how to maintain a visually coherent and engaging form while ensuring all circuits are reliably connected and each component functions properly.

2. Data Barriers
The project depends on extracting data directly from participants (images, speech, gestures). This raises the question of user willingness: will audiences consent to having their identity abstracted, tokenized, or classified? Without voluntary participation, the interactive dimension of the work cannot unfold.

3. Conceptual Barriers
Communicating the main ideas and processes of the project is itself a challenge. Many visitors may not be familiar with AI algorithms or the artistic strategies of abstraction and physicalization. We must design explanatory layers — visual, textual, or experiential — that make the project accessible without oversimplifying its critical intentions.

Map and diagrams
of the computational challenges of the project

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Diagram
parsons orphans