Meaning ● Algorithmic Inequality, within the scope of Small and Medium-sized Businesses (SMBs), embodies the systematic biases inadvertently embedded within automated systems utilized for core business functions. Such biases, when overlooked during the implementation of AI-driven tools, can negatively affect critical business processes impacting decisions from loan approvals to marketing strategies, disproportionately hindering the growth and operational efficiencies of particular SMB segments. These disparities, driven by skewed data sets or flawed algorithms, affect SMBs’ ability to access capital, target customers, and optimize their operations, leading to potential revenue loss and skewed market opportunities. Moreover, the automation promise aimed at leveling the playing field instead risks exacerbating existing economic disparities.
