Good for the Planet, Bad for Me? Intended and Unintended Consequences of AI Energy Consumption Disclosure

TL;DR

This study investigates how energy consumption disclosure influences user choices between small and large language models, revealing effective nudging but also perceptual biases and unchanged subsequent behaviors.

Contribution

It provides empirical evidence on the effectiveness and psychological effects of energy consumption disclosure in AI, highlighting both benefits and unintended consequences.

Findings

ECD increases likelihood of choosing energy-efficient SLM by over 12%

Choice of SLM does not significantly change subsequent user behavior

Eco-friendly choice leads to lower satisfaction and perceived quality

Abstract

To address the high energy consumption of artificial intelligence, energy consumption disclosure (ECD) has been proposed to steer users toward more sustainable practices, such as choosing efficient small language models (SLMs) over large language models (LLMs). This presents a performance-sustainability trade-off for users. In an experiment with 365 participants, we explore the impact of ECD and the perceptual and behavioral consequences of choosing an SLM over an LLM. Our findings reveal that ECD is a highly effective measure to nudge individuals toward a pro-environmental choice, increasing the odds of choosing an energy efficient SLM over an LLM by more than 12. Interestingly, this choice did not significantly impact subsequent behavior, as individuals who selected an SLM and those who selected an LLM demonstrated similar prompt behavior. Nevertheless, the choice created a perceptual bias. A placebo effect emerged, with individuals who selected the "eco-friendly" SLM reporting significantly lower satisfaction and perceived quality. These results highlight the double-edged nature of ECD, which holds critical implications for the design of sustainable human-computer interactions.