Bias in Green AI Addressing Disparities in Data and Algorithms

Abstract

Green AI, a paradigm focused on sustainable and energy-efficient artificial intelligence, holds immense promise for advancing environmental and social equity goals. However, biases inherent in data and algorithms can perpetuate or even exacerbate existing disparities, undermining these objectives. This chapter explores the critical intersection of bias and Green AI, examining how inequities arise in the development and deployment of AI systems designed for sustainability. By analyzing case studies and recent research, the chapter highlights the implications of biased datasets, algorithmic decisions, and accessibility gaps. It also proposes strategies for mitigating bias, such as ethical AI frameworks, diverse data collection practices, and community-driven approaches. Addressing these challenges is essential to ensure that Green AI contributes to an equitable and inclusive future while meeting its sustainability goals.