An organizational learning approach to perceiving and addressing algorithmic bias in agricultural settings

Organizations are deploying artificial intelligence (AI) to improve decision-making and performance. AI-enabled systems are used to automate the decision-making process or assist human choice by providing algorithmically generated information through predictive analytics and recommendations. However, the ability of these systems to improve organizational performance is constrained by biases within the algorithms. This study proposes to use organizational learning as a theoretical lens to understand how users perceive and respond to these biases using their experiential learning and cognitive search processes. The research is set within the agricultural context, as farm organizations are increasingly adopting AI-enabled systems to improve agricultural productivity and sustainability. However, because of complexities associated with the natural environment, algorithmic biases in the recommendation could threat these outcomes. The study proposes to conduct multiple case studies to explore how users of AI-enabled agricultural systems perceive algorithmic bias and develop coping mechanisms to improve agricultural performance. Keywords Algorithmic bias, agriculture, artificial intelligence, cognitive search, experiential learning.