Secret Use of Large Language Model (LLM) (2409.19450v2)

Abstract: The advancements of LLMs have decentralized the responsibility for the transparency of AI usage. Specifically, LLM users are now encouraged or required to disclose the use of LLM-generated content for varied types of real-world tasks. However, an emerging phenomenon, users' secret use of LLM, raises challenges in ensuring end users adhere to the transparency requirement. Our study used mixed-methods with an exploratory survey (125 real-world secret use cases reported) and a controlled experiment among 300 users to investigate the contexts and causes behind the secret use of LLMs. We found that such secretive behavior is often triggered by certain tasks, transcending demographic and personality differences among users. Task types were found to affect users' intentions to use secretive behavior, primarily through influencing perceived external judgment regarding LLM usage. Our results yield important insights for future work on designing interventions to encourage more transparent disclosure of the use of LLMs or other AI technologies.

Analyzing the Implications and Motivations Behind Secret Use of LLMs

The paper "Secret Use of LLMs" explores the phenomenon where users consciously opt to obscure their employment of LLMs across various tasks. Through a methodologically rigorous exploration using mixed methods, the authors illuminate the contexts and motivations driving this secretive behavior, and its implications for AI transparency.

Methodology and Key Findings

The paper employs a two-pronged approach: an exploratory survey capturing 125 real-world instances of secret LLM use and a controlled experiment involving 300 users. The research identifies critical scenarios where secretive behavior manifests, such as academic writing, work tasks, and social interactions. Notably, reasons for concealment include perceived inadequacies, moral doubts, and fears of external judgment.

Survey Results: From the survey, the contexts for concealment ranged from creative writing to sensitive topics, with motivations including issues of self-competence and anticipated social stigma. The findings underscore that users' motivations stem not only from internal self-assessment but also from anticipated external evaluations.

Experimental Findings: The experiment highlights that task type, rather than individual differences, primarily influences concealment intentions. Mediation analysis elucidates that perceived external judgments significantly drive this behavior, suggesting social norms heavily influence users' decision-making in hiding LLM usage.

Implications for AI Transparency

The secretive use of LLMs presents a clear challenge to the principle of AI transparency, particularly in domains where the integrity of LLM outputs is critical, such as academia and professional environments. As LLMs become more ingrained in everyday tasks, ensuring transparency is crucial for mitigating biases and misinformation inherent in AI-generated content.

The paper emphasizes that interventions to foster transparency must be context-sensitive and address both internal and external drivers of concealment. Strategies such as regulatory frameworks, nudges promoting community norms, and enhancements in AI literacy could play pivotal roles.

Future Directions

Future research should focus on examining the nuanced interplay between societal norms and individual privacy, particularly in diverse cultural contexts. Additionally, the development of mechanisms to ensure standardized disclosure of LLM usage could help bridge gaps in transparency.

The emotional stress associated with secret use points to potential well-being concerns for users, warranting further exploration of its psychological impacts. A deeper understanding of how users balance privacy with communal transparency obligations could provide insights into designing better interventions.

Conclusion

The paper offers a comprehensive exploration into why users might conceal their use of LLMs, shedding light on the complex interplay between personal judgment and perceived societal norms. As AI continues to permeate various facets of life, fostering an environment of accountability and transparency will be critical in harnessing its benefits responsibly. The nuanced insights from this research pave the way for developing strategies that encourage open, ethical AI use.