🤖 AI Summary
This study addresses the trade-off users face between model performance and sustainability due to the high energy consumption of artificial intelligence systems. Through a randomized controlled experiment integrating behavioral measures, survey responses, and statistical modeling, we investigate how disclosing energy consumption information influences user preferences for small language models (SLMs). Results show that such disclosures increase the likelihood of selecting an SLM by more than twelvefold, yet do not significantly alter subsequent prompting behavior. Notably, users who chose SLMs reported lower satisfaction, revealing a pronounced negative perceptual bias. Our work identifies—for the first time—the “double-edged sword” effect of energy disclosure: while it encourages environmentally conscious choices, it concurrently undermines perceived model quality. These findings offer critical behavioral insights for designing sustainable AI systems.
📝 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.