Biases in AI Systems
Ramya Srinivasan, Ajay Chander
Abstract
This article provides an organization of various kinds of biases that can occur in the AI pipeline starting from dataset creation and problem formulation to data analysis and evaluation. It highlights the challenges associated with the design of bias-mitigation strategies, and it outlines some best practices suggested by researchers. Finally, a set of guidelines is presented that could aid ML developers in identifying potential sources of bias, as well as avoiding the introduction of unwanted biases. The work is meant to serve as an educational resource for ML developers in handling and addressing issues related to bias in AI systems.
Topics & Concepts
Computer sciencePipeline (software)Set (abstract data type)Resource (disambiguation)Data scienceWork (physics)Risk analysis (engineering)EngineeringBusinessMechanical engineeringProgramming languageComputer networkExplainable Artificial Intelligence (XAI)Ethics and Social Impacts of AIMachine Learning and Data Classification