Misinformation by Omission: The Need for More Environmental Transparency in AI

TL;DR

This paper highlights the critical need for increased environmental transparency in AI to combat misinformation about its ecological impacts, emphasizing data sharing and responsible communication.

Contribution

It identifies misconceptions about AI's environmental impact, analyzes their origins, and proposes transparency-focused recommendations for developers and policymakers.

Findings

Misconceptions about AI's environmental impact are widespread.

Lack of transparency contributes to misinformation.

Recommendations for improving data transparency are provided.

Abstract

In recent years, Artificial Intelligence (AI) models have grown in size and complexity, driving greater demand for computational power and natural resources. In parallel to this trend, transparency around the costs and impacts of these models has decreased, meaning that the users of these technologies have little to no information about their resource demands and subsequent impacts on the environment. Despite this dearth of adequate data, escalating demand for figures quantifying AI's environmental impacts has led to numerous instances of misinformation evolving from inaccurate or de-contextualized best-effort estimates of greenhouse gas emissions. In this article, we explore pervasive myths and misconceptions shaping public understanding of AI's environmental impacts, tracing their origins and their spread in both the media and scientific publications. We discuss the importance of data transparency in clarifying misconceptions and mitigating these harms, and conclude with a set of recommendations for how AI developers and policymakers can leverage this information to mitigate negative impacts in the future.