Situating the Work Within an Algorithmic Exhibition
Following earlier consultations, it became clear that the uncertainty in my prototypes stemmed from the lack of clarity about the final form of work. Instead of continuing to develop isolated experiments and prototypes, I began envisioning the project as a situated exhibit within a larger exhibition about algorithms. This shift reframed my thinking.
This week, I designed the exhibition floor plan, and created a 3D model of the space in Blender. Modelling the environment allowed me to spatially test how the works might coexist, how audiences would move through them, and how each installation contributes to a cohesive narrative about algorithmic systems.
Artist or Curator?
This week's discussion with Andreas fundamentally shifted how I understand my role within the exhibition. The question that surfaced was deceptively simple, yet conceptually loaded. Am I just an artist presenting a singular work, or am I also the curator who is responsible for shaping and framing the entire exhibition?
At first, I had imagined my project as a standable exhibition. Something immersive, self-contained, and conceptually strong enough to exist on its own. However, I realised it would be more impactful if situated within a larger exhibition themed around algorithms. Algorithms do not operate in isolation; they function within systems. Positioning my work within a broader algorithmic context mirrors this logic.
As the artist, I develop the installations. I envision multiple installations rather than one singular piece. Each installation isolates a specific pillar such as datafication, feedback loops, while collectively forming a cohesive experience about Algorithmic Identity.
Designing the floor plan and modelling the 3D space helped me understand how visitors might move through these installations and encounter them sequentially. The exhibition therefore becomes more than a display; it becomes a structured environment where audiences experience how algorithmic systems construct identity.
Keyterms Recap
There are two types of data collected to form the data double, biometric data and behavioural data. Biometric data comes from systems like facial recognition, which map measurable features such as the distance between the eyes, the contour of the lips, or the shape of the nose. Behvaioural data, on the other hand, is gathered from the user's digital interactions such as what they click, like, share, upload, or browse. While biometric data captures how the user looks, behavioural data captures how they act. Together, they form the raw material of Algorithmic Identity.
When these two streams of data are combines, they are processed by algorithms to construct an Algorithmic Identity which is a profile inferred by AI systems. This identity is not a direct reflection of who the user believes they are, but a calculated persona built from patterns, predictions, and probabilities. It shapes what content the user sees, what ads are targetted at them, and even how platforms categorise them. Through this exhibit, I aim to make this invisible construction visible, helping visitors understand how everyday actions accumulate into a system-generated version of the self.
ArtScience Museum / Floorplan
I envision ArtScience Museum as an ideal venue for exhibiting this project because its curatorial direction consistently bridges technology, art, and speculative futures. The museum frequently stages immersive, interactive exhibitions that explore digital systems, artificial intelligence, and data-driven environments. These contexts closely aligned with my investigation into Algorithmic Identities. Its audience is also diverse, attracting both art-goers, and individuals interested in science and innovation. Situating my work within a space strengthens its relevance, positioning it not just as a design project, but as a critical reflection on how algorithmic systems shape contemporary life.
To situate my work within a real context, I search for ArtScience Museum's Basement 2 Floorplan and studied how the larger exhibition might be organised spatially. Rather than designing in isolation, I mapped my exhibit directly onto the exisiting layout. I chose Section 5, placing it before the exit. It is an intentional move as visitors would have already encountered multiple works, allowing my exhibit to function as a moment of reflection and systhesis before departure. It acts as a near-concluding experience, prompting visitors to reconsider what they have seen through the lens of Algorithmic Identity.
Using existing exhibition floorplans as references, I began planning how I wanted my own exhibit to unfold within the allocated space. Having a real spatial template was extremely helpful, as it allowed me to think carefully about installation placements, circulation paths, etc. I designed the layout to guide visitors through the three sections, biometric data, behavioural data, and Algorithmic Identity as a narrative progression. The spatial arrangement supports this conceptual flow, ensuring that each installation builds upon the previous one, resulting in a coherent and immersive experience, rather than three disconnected works.
Installations Mock-up
| Installation | Input (Data Type) | System Process | Output / Purpose |
|---|---|---|---|
| ① Biometric Data | Facial features (eyes, nose, lips), landmark coordinates, motion values | Live camera feed processed through TouchDesigner + MediaPipe. Facial regions boxed and translated into measurable numerical values. | Converts personal facial features into machine-readable data, exposing how biometric systems reduce identity into extractable metrics. |
| ② Behavioural Data | Clicks, interaction frequency, dwell time, navigation patterns | Participants engage with World of Brainrot, generating continuous behavioural metrics through interaction. | Demonstrates how casual engagement becomes data production, revealing the extraction of value from attention and repetition. |
| ③ Construction of Algorithmic Identity | Biometric metrics + behavioural metrics | System merges extracted datasets into a unified algorithmic model, displayed across a multi-screen LED wall. | Visualises the synthesis of fragmented data into a coherent — yet reductive — digital persona constructed by algorithmic systems. |
3D Modelling / Blender
I constructed a 3D model of the exhibition in Blender to visualise spatial relationships, circulation flow, pillar placements, and installation sequencing. This digital mock-up allowed me to test spatial and installation arrangments, and narrative progression before translating the concept into a physical exhibition setting.
Installations 3D
Blender Process
Challenges Faced
Creating the 3D exhibition model in Blender was far more challenging than I anticipated, especially since I was new to 3D modelling using Blender. I spent around 3 days trying to create this entire model. Shoutout to Carissa for teaching me more about Blender, rendering solutions, as well as creating plastic PBR.
I struggled with basic operations such as selecting and extruding the correct faces, aligning objects accurately to the floorplan, and ensuring surfaces rendered on the intended side. Navigating orientation within a three-dimensional space was particularly frustrating as there were issues with the zoom at times. Distinguishing between global and local axes, camera views, and object origins often disrupted my workflow. Even when the structure was technically complete, the final render felt visually flat and unrefined. The lighting appeared dull, materials lacked depth, and the overall scene did not reflect the clarity I had imagined.
Feedback and Reflection
① Iteration and Spatial Planning
Although creating the 3D model was a strong first step, the exhibition layout requires more iteration. Are the works placed in the right positions? Are the spatial relationships intentional? I need to explore partitions, empty spaces, and circulation more carefully. The use of space should not be accidental. Introducing partitions or subtle boundaries could help structure the audience’s journey and better reflect the conceptual transitions between installations.
② Modes of Interactivity
The interactivity of the installations needs refinements. For the Biometric Data piece I need to think deeper: when, where, and how does the image capture occur? How would participants know what to do? Having clear instructions or intuitive cues like a photobooth could help. If participants recognise a familiar interaction, they may naturally participate.
For the Behavioural Data piece, the display design can be improved. How do we encourage people to click? If there are three screens, do participants tap directly, use a mouse, or engage differently? The interface must feel intuitive. Likewise, using a touchscreen is not advisable due to durability and the practicality.
There is an opportunity to explore the idea inspired from a photobooth where participants are able to print or upload their image with a barcode, which can then be scanned and fed onto the large LED video wall display. This would introduce a stronger interaction loop. However, the spatial design must clearly communicate this flow, and is something that I need to further work on.
③ Audience Engagement and Enticement
A huge concern to consider is how can I encourage participants to slow down? How do I design a space that entices visitors to sit and observe their generated algorithmic identity? The exhibition must actively invite participation rather than assume it.
④ A + B = C
There is a huge emphasis on the importance of clearly communicating the logic behind the work, how inputs (A + B) lead to outputs (C). This equation should not only be conceptually stated but expressed through spatial and experiential design. Furthermore, I need to consider how this logic translates into other mediums, such as a publication or website. Successful communication must extend beyond the exhibition.