PREVENTING ALGORITHMIC BIAS: EXPLORING AND ANALYZING EXAMPLES

This research paper examines various instances of Algorithmic Bias and their adverse effects on individuals and systems. It investigates the connection between these biases and existing laws while proposing solutions to mitigate algorithmic bias. The paper begins by elucidating the four characteristics of an algorithm, namely finiteness, unambiguity, well-defined inputs and outputs, and feasibility. Furthermore, it provides an explanation of different types of algorithmic bias, including programmed bias, training data bias, interpretation bias, algorithmic focus bias, algorithmic processing bias, and transfer context bias. The study delves into three prominent examples of algorithmic bias within the domains of medicine, law enforcement, and education. Additionally, the paper addresses racial and financial bias concerns, examines relevant privacy laws, and explores strategies for coexisting with algorithms.