Algorithmic Identity
ROLE :
Creative technologist, Design researcher
TIMELINE :
8 months
TOOLS :
claude code, vercel, github, ChatGPT.
Presented at
The Problem
Algorithms now shape what we see and do every day. Good design should work for the people living inside these systems, not just the companies running them. How do we build tools that show people what an algorithm has inferred about them, so they can engage with that system critically rather than absorb it passively?
Social media algorithms curate billions of feeds daily, shaping what we see and how we think, yet users remain passive recipients. Recent platform transparency efforts show what an algorithm did, but not the personalised interpretation it has built from our attention patterns. This research closes that gap: taking the concept of algorithmic identity (Cheney-Lippold, 2017) and translating it into something narrative and legible, rather than technical. The findings translate directly into design implications for personalisation systems, and for value-driven design more broadly, treating a system's relationship to a user's sense of self as something to be designed for deliberately, not left as a side effect of engagement optimisation.
Research Method
Method:
A Research through Design (RtD) study, building a design probe workshop with 25 participants.
Process:
Upload 3 screenshots from instagram explore GPT-4o generates personalised narrative portrait participants reflect on their alorithmic identity. It enable participants to examine the digital selves constantly being constructed about them as highlighted by Shoshana Zuboff on surveillance capitalism.
"It helped me name the difference between who I think I am and how my online behaviour quietly shaped another version of me." - P13
Key Findings
Conceptual Gifting: Users gained agency not from seeing their data, but from receiving language for previously inarticulate patterns.
"I couldn't describe my 'aesthetic' or 'visual interest' properly, but the algorithm used words such as 'soft luxury', 'symmetry', 'curator-in-training', and 'domestic intelligence.'" (P24)
Algorithmic Authorship: A smaller group of participants pushed past "does the algorithm see me accurately?" toward a more speculative question: "who do I want the algorithm to help me become?" It's an early, exploratory signal, not yet a widespread pattern, but it reframes the algorithm from a mirror of past behaviour into a possible scaffold for future intention.
"Sometimes I have to 'clean up' the explore page."
— P10
Identity First Design Framework: Ehsan & Riedl (2020) distinguish technical explainability (why this post?) from experiential explainability (what does this say about who I'm becoming?). This study's contribution is showing what experiential explainability actually looks like in practice: narrative interpretation placed alongside, not instead of, data-led transparency.
"I give a goal to the system in the beginning of the year and it matches my content to it— for example, if I want to eat better, I want it to align my content to that." (P25)
Shifts agency from platform to users through reflection on Algorithmic Identity
Makes invisible systems emotionally available
Democratizes algorithmic literacy through accesible language
Why this? Why now?
Recent research on personalised AI persuasion found that a personalised GPT-4 was measurably more persuasive than human debate opponents, while the same model with no personal data performed no better than a human. Systems that know more about us are getting better at influencing us, often without our seeing the profile being built.
In emerging tech, as personalisation deepens and regulation like the EU's Digital Services Act pushes platforms toward greater transparency, how these systems become legible is no longer just a technical question. It's a personal and cultural one. Most explainable AI still focuses on explaining how an algorithm works, not on helping people understand their relationship with it.
Instagram as emotional regulation
Attention shapes Identity
Twelve of 25 participants named distraction, unprompted, as their reason for opening Instagram, more than any other single motivation. For many, the platform functioned less as entertainment and more as a way to regulate mood, escape, or avoid something else entirely.
Participants also became aware that small behaviours, lingering, liking, scrolling, contribute to a second, algorithmically-constructed version of themselves online, one they don't normally get to see.
"I don't even know anymore… maybe inspiration? Being on IG makes me feel horrible and I feel so much better off it." — P21
"It reveals a side of myself I don't see because of my ego." — P17
Reflection comes from language, not data
Productive discomfort creates awareness
The strongest reactions came when the AI gave participants vocabulary for something they already felt but hadn't been able to articulate, a phenomenon this research terms conceptual gifting.
Even inaccurate interpretations prompted reflection for some participants, who found themselves questioning why the algorithm had read them that way, rather than simply dismissing it.
"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
"It helped me name the difference between who I think I am and how my online behavior quietly shapes another version of me." — P13