Algorithmic Identity
ROLE :
Creative technologist, Design researcher
TIMELINE :
8 months
TOOLS :
claude code, vercel, github, ChatGPT.
Project Overview
How can design enable experiential explainability of algorithms through narration and reflection?
Drawing from the work of John Cheney-Lippold on algorithmic identity, this Research-through-Design study developed within Human - Computer Interaction explored Explainable AI (XAI) through a critical lense and inquired how design probes can make invisible algorithmic profiling emotionally and personally legible.
Using participants’ Instagram Explore pages, the study generated AI-written “algorithmic identity portraits” that reflected back how platforms interpret users through their scrolling patterns, attention, and behaviour. Rather than explaining how algorithms work technically, the project investigated how narration, reflection, and productive discomfort could help people critically examine the digital selves constantly being constructed about them as highlighted by Shoshana Zuboff on surveillance capitalism.
This study was undertaken as part of my Master’s dissertation at Central Saint Martins.
What I did
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Designed and conducted a Research-through-Design study with 25 participants
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Built a narrative-based AI reflection probe using GPT-4o
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Led qualitative research, thematic analysis, and synthesis
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Developed an “identity-first” framework for reflective explainable AI
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Translated findings into platform and product design recommendations
How I did it
Participants uploaded screenshots of their Instagram Explore page into a custom GPT-4o prompt. The system generated a second-person narrative interpretation across themes like emotional patterns, identity signals, blind spots, and behavioural tendencies.
The responses before and after the interaction were analysed through thematic analysis to understand how people reacted to seeing an AI-generated portrait of themselves.
Why it matters
Every scroll, pause, like, and interaction contributes to a hidden behavioural profile continuously constructed by platforms. Research shows that messages personalised through behavioural data can be significantly more persuasive than non-personalised ones, reinforcing feedback loops, echo chambers, and increasingly narrow worldviews.
And the uncomfortable part is: users rarely get to see the profile being built about them. The algorithm learns what comforts you, what keeps your attention, what aspirations or vulnerabilities repeatedly surface through your behaviour. Shoshana Zuboff describes this as a core mechanism of surveillance capitalism, transforming lived experience into behavioural prediction.
As emerging regulations like the EU AI Act push for greater transparency, the question of how these systems should become legible is no longer just technical, but deeply personal and cultural. Yet most explainable AI systems focus on explaining how algorithms work, rather than helping people understand their relationship with them.
Key findings
Algorithms act as emotional mirrors
Many participants realised they used Instagram less for entertainment and more for emotional regulation, distraction, or escape.
"I don't even know anymore… maybe inspiration? Being on IG makes me feel horrible and I feel so much better off it." — P21
Attention shapes identity
Participants became aware that small behaviours, lingering, liking, scrolling quietly construct a second version of themselves online.
"It reveals a side of myself I don't see because of my ego." — P17
Reflection comes from language, not data
The strongest reactions came when AI gave participants vocabulary for things they already felt but could not previously articulate a phenomenon termed “conceptual gifting.”
"It felt like someone put what I have been struggling with lately, for quite a while in fact, into words. It was a call out, an invitation to consider what I knew I needed to consider but did not have proof for. Or permission for." — P15
Productive discomfort creates awareness
Even inaccurate interpretations triggered reflection, because users questioned why the algorithm perceived them in a particular way.
"It helped me name the difference between who I think I am and how my online behavior quietly shapes another version of me." — P13
Industry
Recommendations
Move beyond dashboards
Platforms should complement analytics and transparency tools with narrative-based reflections that help users understand behavioural patterns in human terms.
Design for reflection, not optimisation
AI systems should create moments of pause, awareness, and self-questioning, not only engagement and retention.
Make echo chambers emotionally visible
Narrative interventions can help users feel how narrow or repetitive their feeds have become, encouraging healthier digital habits.
Enable intentional algorithmic agency
Future systems could allow users to actively shape the identity algorithms construct about them, rather than passively reinforcing past behaviour.