AI driven Algorithms, search and recommendations: Impact on Artisans

Artisans trapped behind digital curtains

In this pillar, I delve into the complex ways artificial intelligence and various algorithms shape the economic realities of artisans and micro-entrepreneurs. While AI promises efficiency and wider reach, its application in search, e-commerce, and credit scoring can inadvertently create an "algorithmic divide," perpetuating marginalization rather than fostering inclusion.

My research examines how recommendation engines on large e-commerce platforms can inadvertently favor established brands or digitally-savvy sellers, making it difficult for small, craft-based businesses to gain visibility. Similarly, credit scoring algorithms, while efficient, often lack the nuanced data points (like social capital, informal transaction history, or seasonal income patterns) that are crucial for accurately assessing the creditworthiness of nano-enterprises.

I explore how these systems can hold small businesses captive within certain classifications, limiting their access to capital and markets. Understanding these mechanisms is vital for designing truly equitable digital ecosystems. My work aims to uncover these hidden impacts and advocate for algorithmic transparency and fairness that serves the most vulnerable.

Key Questions Explored:

• How do AI-driven recommendation algorithms shape market access for informal artisans?

• What are the biases in credit scoring algorithms that exclude nano and micro enterprises?

• How can digital identification and classification systems be designed to promote, rather than hinder, business mobility?

• What agency do first-mile entrepreneurs have to influence their digital footprint and algorithmic outcomes?