Publication Details
Issue: Vol 3, No 2 (2026)
ISSN: 2997-3961
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Abstract

Every time you open your phone, an algorithm is already deciding what you'll see next. These systems - the ones powering your social feeds, search results and streaming recommendations - are often described as neutral tools that simply "learn your preferences." But here's the uncomfortable truth: they don't just reflect who you are. They shape who you're becoming. This paper argues that modern algorithms don't passively predict our choices - they actively structure the environments where those choices happen. Drawing on research from data science, behavioral psychology and socio-technical studies, we examine how recommendation systems actually work under the hood and what that means for all of us. We explore how optimization-driven models, feedback loops and platform economics combine to influence behavior - not just individually, but collectively. Ultimately, we frame the relationship between humans and algorithms as a co-evolutionary process, one that raises urgent questions about responsibility, governance and the kind of digital society we're building.

Keywords
Algorithmic influence recommendation systems behavioral reinforcement algorithmic bias attention economy personalization feedback loops socio-technical systems