Authors: Aswathanarayana A, Dr. Muddagangaiah K C
Abstract: Artificial intelligence now sits at the heart of e-commerce, shaping which products show up on our screens and, in turn, who makes the sale. In India, where online shopping is booming, there’s a big question hanging in the air: Are these algorithms fair, especially to smaller or local sellers? Big brands have muscle, but do they also get an algorithmic boost, pushing the little guys further into the background? This study digs into that, focusing on Indian platforms like Amazon India and Flipkart. We got hands-on—scraping product data, building a bunch of different user profiles, and running the numbers using fairness metrics. Our goal? To see if these systems consistently push big brands to the top while leaving small or regional sellers in the dust. We set up controlled accounts with different browsing histories and fake demographics to mimic a real mix of shoppers. Then, we tracked which products showed up, where, and for whom. On top of the stats, we talked to small sellers themselves to hear how they experience the system, what they see, and how it’s hitting their bottom line. What did we find? The numbers point to a clear visibility bias: established brands get more exposure, and small sellers get crowded out. This isn’t always intentional, but it’s real—and it can deepen market inequality without anyone really noticing. To fix it, we lay out a re-ranking framework that levels the playing field, giving smaller sellers a fairer shot without tanking recommendation quality. At the end of the day, our work adds to the bigger debate on ethical AI and fair digital markets, showing both policymakers and e-commerce managers what’s really happening—and what they can do about it.
