MirrorMind is a 12-week longitudinal study measuring how AI recommendation algorithms reshape your expressed identity over time — using our novel Identity Drift Score.
We introduce the Identity Drift Score (IDS) — a composite NLP metric tracking three dimensions of identity change: semantic embedding distance, topic entropy shift, and lexical diversity loss.
Both groups drifted from baseline. The difference was the shape of that drift — treatment group IQR was 15.4× tighter than control (Levene p = 0.033). The algorithm makes everyone drift the same way, regardless of where they started.
Novel composite metric combining semantic embedding distance, topic entropy, and lexical diversity loss into a single, interpretable number.
4 collection waves over 12 weeks. Watch your IDS evolve in real-time as you complete weekly survey responses.
Under the hood: NMF topic modeling, cosine similarity on sentence embeddings, KL-divergence, MTLD lexical diversity scoring.
Fully anonymised at ingestion. SHA-256 participant IDs. GDPR-compliant data exports only. Right to withdraw at any time.
Results from 60 participants across 12 weeks, with Bonferroni-corrected statistical validation.
Treatment group IQR was 15.4× tighter than control (0.082 vs 1.263) — Levene F(1,58)=4.80, p=0.033. The algorithm produced uniform, convergent drift across participants who started differently.
Both groups drifted significantly from baseline (Wilcoxon p<0.001 for both). The finding is not about magnitude — it's about consistency. Control drift was individual. Treatment drift was convergent.
IDS slope variance: treatment SD=0.135 vs control SD=0.399 (Levene p=0.039). The homogenization effect holds for rate of change, not just final IDS.
Join the study and receive a personalised Identity Drift Score report after 12 weeks. All data is anonymised and IRB-approved.