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The Role of Inclusion, Control, and Ownership in Workplace AI-Mediated Communication

2024-05-11
Kowe Kadoma , Marianne Aubin Le Quéré , Xiyu Jenny Fu +3 more
Given large language models' (LLMs) increasing integration into workplace software, it is important to examine how biases in the models may impact workers. For example, stylistic biases in the language … Given large language models' (LLMs) increasing integration into workplace software, it is important to examine how biases in the models may impact workers. For example, stylistic biases in the language suggested by LLMs may cause feelings of alienation and result in increased labor for individuals or groups whose style does not match. We examine how such writer-style bias impacts inclusion, control, and ownership over the work when co-writing with LLMs. In an online experiment, participants wrote hypothetical job promotion requests using either hesitant or self-assured auto-complete suggestions from an LLM and reported their subsequent perceptions. We found that the style of the AI model did not impact perceived inclusion. However, individuals with higher perceived inclusion did perceive greater agency and ownership, an effect more strongly impacting participants of minoritized genders. Feelings of inclusion mitigated a loss of control and agency when accepting more AI suggestions.
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Large Language Models in Qualitative Research: Can We Do the Data Justice?

2024-10-09
Hope Schroeder , Marianne Aubin Le Quéré , Casey Randazzo +2 more
Qualitative researchers use tools to collect, sort, and analyze their data. Should qualitative researchers use large language models (LLMs) as part of their practice? LLMs could augment qualitative research, but … Qualitative researchers use tools to collect, sort, and analyze their data. Should qualitative researchers use large language models (LLMs) as part of their practice? LLMs could augment qualitative research, but it is unclear if their use is appropriate, ethical, or aligned with qualitative researchers' goals and values. We interviewed twenty qualitative researchers to investigate these tensions. Many participants see LLMs as promising interlocutors with attractive use cases across the stages of research, but wrestle with their performance and appropriateness. Participants surface concerns regarding the use of LLMs while protecting participant interests, and call attention to an urgent lack of norms and tooling to guide the ethical use of LLMs in research. Given the importance of qualitative methods to human-computer interaction, we use the tensions surfaced by our participants to outline guidelines for researchers considering using LLMs in qualitative research and design principles for LLM-assisted qualitative data analysis tools.
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Examining the Prevalence and Dynamics of AI-Generated Media in Art Subreddits

2024-10-09
Hana Matatov , Marianne Aubin Le Quéré , Ofra Amir +1 more
Broadly accessible generative AI models like Dall-E have made it possible for anyone to create compelling visual art. In online communities, the introduction of AI-generated content (AIGC) may impact community … Broadly accessible generative AI models like Dall-E have made it possible for anyone to create compelling visual art. In online communities, the introduction of AI-generated content (AIGC) may impact community dynamics by shifting the kinds of content being posted or the responses to content suspected of being generated by AI. We take steps towards examining the potential impact of AIGC on art-related communities on Reddit. We distinguish between communities that disallow AI content and those without a direct policy. We look at image-based posts made to these communities that are transparently created by AI, or comments in these communities that suspect authors of using generative AI. We find that AI posts (and accusations) have played a very small part in these communities through the end of 2023, accounting for fewer than 0.2% of the image-based posts. Even as the absolute number of author-labelled AI posts dwindles over time, accusations of AI use remain more persistent. We show that AI content is more readily used by newcomers and may help increase participation if it aligns with community rules. However, the tone of comments suspecting AI use by others have become more negative over time, especially in communities that do not have explicit rules about AI. Overall, the results show the changing norms and interactions around AIGC in online communities designated for creativity.
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"Ownership, Not Just Happy Talk": Co-Designing a Participatory Large Language Model for Journalism

2025-01-28
Emily Tseng , Meg Young , Marianne Aubin Le Quéré +2 more
Journalism has emerged as an essential domain for understanding the uses, limitations, and impacts of large language models (LLMs) in the workplace. News organizations face divergent financial incentives: LLMs already … Journalism has emerged as an essential domain for understanding the uses, limitations, and impacts of large language models (LLMs) in the workplace. News organizations face divergent financial incentives: LLMs already permeate newswork processes within financially constrained organizations, even as ongoing legal challenges assert that AI companies violate their copyright. At stake are key questions about what LLMs are created to do, and by whom: How might a journalist-led LLM work, and what can participatory design illuminate about the present-day challenges about adapting ``one-size-fits-all'' foundation models to a given context of use? In this paper, we undertake a co-design exploration to understand how a participatory approach to LLMs might address opportunities and challenges around AI in journalism. Our 20 interviews with reporters, data journalists, editors, labor organizers, product leads, and executives highlight macro, meso, and micro tensions that designing for this opportunity space must address. From these desiderata, we describe the result of our co-design work: organizational structures and functionality for a journalist-controlled LLM. In closing, we discuss the limitations of commercial foundation models for workplace use, and the methodological implications of applying participatory methods to LLM co-design.
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"Ownership, Not Just Happy Talk": Co-Designing a Participatory Large Language Model for Journalism

2025-01-28
Emily Tseng , Meg Young , Marianne Aubin Le Quéré +2 more
Journalism has emerged as an essential domain for understanding the uses, limitations, and impacts of large language models (LLMs) in the workplace. News organizations face divergent financial incentives: LLMs already … Journalism has emerged as an essential domain for understanding the uses, limitations, and impacts of large language models (LLMs) in the workplace. News organizations face divergent financial incentives: LLMs already permeate newswork processes within financially constrained organizations, even as ongoing legal challenges assert that AI companies violate their copyright. At stake are key questions about what LLMs are created to do, and by whom: How might a journalist-led LLM work, and what can participatory design illuminate about the present-day challenges about adapting ``one-size-fits-all'' foundation models to a given context of use? In this paper, we undertake a co-design exploration to understand how a participatory approach to LLMs might address opportunities and challenges around AI in journalism. Our 20 interviews with reporters, data journalists, editors, labor organizers, product leads, and executives highlight macro, meso, and micro tensions that designing for this opportunity space must address. From these desiderata, we describe the result of our co-design work: organizational structures and functionality for a journalist-controlled LLM. In closing, we discuss the limitations of commercial foundation models for workplace use, and the methodological implications of applying participatory methods to LLM co-design.
View PDF Chat

Examining the Prevalence and Dynamics of AI-Generated Media in Art Subreddits

2024-10-09
Hana Matatov , Marianne Aubin Le Quéré , Ofra Amir +1 more
Broadly accessible generative AI models like Dall-E have made it possible for anyone to create compelling visual art. In online communities, the introduction of AI-generated content (AIGC) may impact community … Broadly accessible generative AI models like Dall-E have made it possible for anyone to create compelling visual art. In online communities, the introduction of AI-generated content (AIGC) may impact community dynamics by shifting the kinds of content being posted or the responses to content suspected of being generated by AI. We take steps towards examining the potential impact of AIGC on art-related communities on Reddit. We distinguish between communities that disallow AI content and those without a direct policy. We look at image-based posts made to these communities that are transparently created by AI, or comments in these communities that suspect authors of using generative AI. We find that AI posts (and accusations) have played a very small part in these communities through the end of 2023, accounting for fewer than 0.2% of the image-based posts. Even as the absolute number of author-labelled AI posts dwindles over time, accusations of AI use remain more persistent. We show that AI content is more readily used by newcomers and may help increase participation if it aligns with community rules. However, the tone of comments suspecting AI use by others have become more negative over time, especially in communities that do not have explicit rules about AI. Overall, the results show the changing norms and interactions around AIGC in online communities designated for creativity.
View PDF Chat

Large Language Models in Qualitative Research: Can We Do the Data Justice?

2024-10-09
Hope Schroeder , Marianne Aubin Le Quéré , Casey Randazzo +2 more
Qualitative researchers use tools to collect, sort, and analyze their data. Should qualitative researchers use large language models (LLMs) as part of their practice? LLMs could augment qualitative research, but … Qualitative researchers use tools to collect, sort, and analyze their data. Should qualitative researchers use large language models (LLMs) as part of their practice? LLMs could augment qualitative research, but it is unclear if their use is appropriate, ethical, or aligned with qualitative researchers' goals and values. We interviewed twenty qualitative researchers to investigate these tensions. Many participants see LLMs as promising interlocutors with attractive use cases across the stages of research, but wrestle with their performance and appropriateness. Participants surface concerns regarding the use of LLMs while protecting participant interests, and call attention to an urgent lack of norms and tooling to guide the ethical use of LLMs in research. Given the importance of qualitative methods to human-computer interaction, we use the tensions surfaced by our participants to outline guidelines for researchers considering using LLMs in qualitative research and design principles for LLM-assisted qualitative data analysis tools.
View PDF Chat

The Role of Inclusion, Control, and Ownership in Workplace AI-Mediated Communication

2024-05-11
Kowe Kadoma , Marianne Aubin Le Quéré , Xiyu Jenny Fu +3 more
Given large language models' (LLMs) increasing integration into workplace software, it is important to examine how biases in the models may impact workers. For example, stylistic biases in the language … Given large language models' (LLMs) increasing integration into workplace software, it is important to examine how biases in the models may impact workers. For example, stylistic biases in the language suggested by LLMs may cause feelings of alienation and result in increased labor for individuals or groups whose style does not match. We examine how such writer-style bias impacts inclusion, control, and ownership over the work when co-writing with LLMs. In an online experiment, participants wrote hypothetical job promotion requests using either hesitant or self-assured auto-complete suggestions from an LLM and reported their subsequent perceptions. We found that the style of the AI model did not impact perceived inclusion. However, individuals with higher perceived inclusion did perceive greater agency and ownership, an effect more strongly impacting participants of minoritized genders. Feelings of inclusion mitigated a loss of control and agency when accepting more AI suggestions.
View PDF Chat
Coauthor Papers Together
Mor Naaman 2
Christin L. Munsch 1
Harini Suresh 1
Ofra Amir 1
Hope Schroeder 1
Sarita Schoenebeck 1
Xiyu Jenny Fu 1
Emily Tseng 1
Hana Matatov 1
Casey Randazzo 1
Kowe Kadoma 1
Danaë Metaxa 1
David Mimno 1
Meg Young 1
Aimee Rinehart 1