Week 7 / Formative Assessment

RPO Development

This week's group consultation helped clarify several conceptual and structural elements in my research proposal. It challenged me to be more specific about my definitions while also identifying where I need to zoom out and hold onto broader framing.

Feedback Breakdown

Refine Research Objective

My current objective leans too heavily into the idea of a exhibition/gallery outcome too early in the process. The suggestion is to keep the "explore" part more generic to leave room for multiple possible outcomes. Moving forward, I have to also clarify what format I am looking at, e.g. archival format, hybrid digital-archival presentation.

Refine Approaches/Methods

My process should begin with small interactive experiments that build toward one or two developed prototypes. These prototypes are not final outcomes but inquiry tools, designed to provoke, test, or reveal specific questions. To strengthen this process, I need to embed structured reflection after each experiment, asking: What did I learn? How does this shape the next step? This cycle of making and reflection ensures that my work remains intentional and iterative. Ultimately, the goal is to focus on one key prototype, clearly articulating its cause and purpose, why it exists, what provocation it responds to, and how it ties back to the broader research question.

Define "Creative Visualisation"

I’ve been using the term creative visualisation but haven’t clearly defined it. This feedback was helpful: Am I talking about data art, speculative diagrams, interface design, or narrative installations? I need to define what creative visualisation means within my project’s context, and how it supports or challenges audience engagement with data.

Strengthen Research Pillars

The pillars of my research need clearer alignment and depth:

⑴ Datafication
I should explore how individuals become data through web-based platforms. Am I dealing with archives, apps, or new tools? Do I want to build an archive, document a process, or develop a tool? I need to decide. The suggestion to clarify whether this is web-based, archival, or app/tool-focused is a crucial framing choice. Right now, I’m interested in web-based archives, but I need to test this through my experiments.

⑵ Feedback Loops + Manipulation
How is this data reused, algorithmically shaped, and how does it shape us back?

⑶ Digital Exhibition Design
This pillar should present data to an audience or serve as a form of archiving. I need to show that I’ve built an understanding of how digital exhibition strategies function in current design/art contexts. Using references like Refik Anadol is a good start—but I should also think about the tools, UI/UX strategies, and affordances involved in digital exhibitions. Needs to show how data is then presented back to audiences or archived.

Double Diamond

The feedback encouraged me to explicitly reference the Double Diamond process. Since making is central to this project, showing that I’m using a structured, iterative framework reinforces my methodological rigour. Each phase can map directly onto my research process and prototyping cycle.

Studio Setup

For the studio setup, I curated a working display of my in-progress experiments, research materials, and prototypes to make my process visible. The space allowed viewers to see how each idea evolved, from early sketches and readings to coded prototypes and visual tests.

Design Factory@SIT Guest Studio Feedback

⑴ Rethinking Data Visualisation
Jeff pointed out that the project “currently reads as data visualisation,” which wasn’t a critique of the visuals, but a reminder that visual output alone isn’t enough. This pushed me to question the function of the visuals: Are they merely representing data, or are they enabling users to understand, negotiate, or even challenge their own algorithmic identity? His feedback made clear that the work must go beyond displaying patterns, it needs to create an active, reflective relationship between the user and the system.

⑵ Reframing Input
He emphasised the importance of thinking more critically about input work: how data is produced, what users give, and whether that giving is intentional or invisible. Instead of relying on explicit actions (e.g., typing, clicking), he encouraged me to explore forms of subtle or ambient input that mirror real-world data extraction. This raises essential questions around consent: When is the user knowingly contributing data? When is the system reading them without their awareness? His feedback reframed input not as a feature, but as the conceptual core of the project.

⑶ Individual ↔ Community
The suggestion to think about the individual in relation to the collective opened a new direction. Jeff proposed ideas like “each person having a plot” or “each user represented as a pixel,” which reframes the project as modular and scalable. Rather than isolated identities, the work could show how many individual traces accumulate into a shared visual system. This aligns with the project’s themes: algorithmic identity is personal, but never private, it always sits within larger patterns. This feedback encouraged me to imagine a space where users shape their own data presence while simultaneously contributing to a collective ecosystem.

⑷ Data Disguise
Aditi’s feedback pushed me to think about how data hides in plain sight, not just in systems, but in the very interfaces we trust. She encouraged me to explore what happens when the project is framed like an app or familiar digital environment, because interfaces often act as disguises: they smooth over discomfort, mask data extraction, and present surveillance as usability. Her comment made me question whether the interface itself could be the experiment. What if the system behaves like a friendly tool at first, only to gradually surface its biases, presumptions, and political underpinnings? How might a moment of friction, confusion, or revelation be intentionally built into the user journey so that the experience exposes the illusions of neutrality embedded in everyday interface design?

Food for Thought

Overall, the feedback is pushing me to think more critically about the philosophy behind the interaction:

• What does it mean to feed a digital garden?
• How do I visualise and materialise the invisible trails of data that shape our algorithmic selves?
• How do I responsibly and provocatively invite users into that system?

For my next steps, I can explore on the following:

• Explore layered input methods: passive (e.g., dwell time), active (e.g., typed reflections), and ambient (e.g., device data).
• Sketch interaction scenarios where individuals see both their “plot” and the impact on the communal space.
• Look at precedents in critical design and data art (e.g., Lauren McCarthy, Mimi Ọnụọha) for approaches to consent, surveillance, and visibility.

This round of feedback really helped shift my focus from output to process, encouraging me to design not just what users see, but how they participate and what that participation means.

Moving Forward

I want to explore the idea of data camouflage, designing an interface that begins as benign, familiar, or even helpful, but gradually reveals the hidden assumptions and classifications operating beneath it. This approach directly connects to my research on algorithmic identities, where identities are not merely represented by algorithms but co-constructed through continuous interpretation of our behaviours, preferences, and data traces.

By letting the interface slowly “unmask” its interpretive mechanisms, the work mirrors how real-world algorithmic systems quietly shape who we become online. The moment of dissonance, when the interface reveals that it has been profiling, predicting, or categorising the user all along, becomes a way to make this co-construction visible. Instead of presenting algorithmic identity as an abstract concept, the experiment allows users to feel how their identity emerges from interactions they assumed were neutral.

This grounding ensures that the piece is not just about interface trickery, but about exposing the subtle, everyday processes through which algorithms participate in producing the self.