 ____   __  ____  ___  __   __ _  ___ 
(  _ \ / _\(  _ \/ __)/  \ (  ( \/ __)
 ) __//    \)   /\__ (  O )/    /\__ \
(__)  \_/\_(__\_)(___/\__/ \_)__)(___/
  __  ____  ____  _  _   __   __ _  ___ 
 /  \(  _ \(  _ \/ )( \ / _\ (  ( \/ __)
(  O ))   / ) __/) __ (/    \/    /\__ \
 \__/(__\_)(__)  \_)(_/\_/\_/\_)__)(___/
        >> NORA_RUI_OVERRIDE_INIT //

Final WEB

01 Problem Statement

>> ERR_SUBJECTIVITY_NOT_FOUND

Artificial intelligence has become an omnipresent phenomenon. Yet the internal logic remains unseen. Public attention fixates on efficiency, while few recognize what it means to become a data point.

This project investigates how human identity is transformed. Labels such as "Asian" / "female" are applied. Subjectivity is deconstructed, fragmented, and redefined.

Fig 1.1: Trevor Palgen & Kate Crawford - Training Human

Fig 1.1: Data Loss

Fig 1.2: Compression

Fig 1.3: Noise

02 Similar Artworks

+--+--+--[ DATA_STREAM ]--+--+--+

Adam Harvey — CV Dazzle

Nam June Paik — TV Buddha

Nota Bene — In order to control

Paolo Cirio — Capture

Rafael Lozano-Hemmer — Zoom Pavilion

Zack Lieberman — Más Que la Cara

The algorithmic gaze is absolute.

Reference Archive (16 Assets Loaded)

03 System Architecture

> SYSTEM_READY

// PROJECT_ARCHIVE_04

================================

RENDER_TARGET_FBX

[INIT] Loading Assets..._

05 EXPECTED_USER_OUTCOMES _

06 Concerns / Limitations

Entering the VOID.

/!\ WARNING /!\
BIAS_DETECTED

> READ_PROTOCOL: ETHICS_V1.0

This project critically examines how AI systems break human identity into fragments, labels, and statistical categories.

Because we use the same computer-vision tools we aim to critique, we remain cautious. Our system does NOT attempt to classify people or store identifiable data.

We acknowledge the risks of bias and misrepresentation. We treat these limitations not as features to optimize, but as conditions to make visible.

// OBJECTIVE: Expose the mechanics of abstraction.