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"I'm categorizing LLM as a productivity tool": Examining ethics of LLM use in HCI research practices

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arxiv 2403.19876 v1 pith:PUSLCVKP submitted 2024-03-28 cs.HC

classification cs.HC
keywords researchethicalmodelsengagingethicsissueslacklanguage
verification ladder T0 review T1 audit T2 compute T3 formal
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Large language models are increasingly applied in real-world scenarios, including research and education. These models, however, come with well-known ethical issues, which may manifest in unexpected ways in human-computer interaction research due to the extensive engagement with human subjects. This paper reports on research practices related to LLM use, drawing on 16 semi-structured interviews and a survey conducted with 50 HCI researchers. We discuss the ways in which LLMs are already being utilized throughout the entire HCI research pipeline, from ideation to system development and paper writing. While researchers described nuanced understandings of ethical issues, they were rarely or only partially able to identify and address those ethical concerns in their own projects. This lack of action and reliance on workarounds was explained through the perceived lack of control and distributed responsibility in the LLM supply chain, the conditional nature of engaging with ethics, and competing priorities. Finally, we reflect on the implications of our findings and present opportunities to shape emerging norms of engaging with large language models in HCI research.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Understanding the LLM-ification of CHI: Unpacking the Impact of LLMs at CHI through a Systematic Literature Review

    cs.HC 2025-01 conditional novelty 7.0 of 10

    A systematic review of 153 CHI papers shows LLMs are used across ten domains, mostly as system engines and in empirical or artifact contributions, with widespread validity and reproducibility concerns.

  2. From Assistance to Autonomy -- A Researcher Study on the Potential of AI Support for Qualitative Data Analysis

    cs.CY 2025-01 conditional novelty 5.0 of 10

    Interviews with 15 HCI researchers show openness to AI in qualitative data analysis under conditions of privacy, control, and reliability, leading to a framework of AI involvement levels from minimal to high.

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