Suggestion Lists vs. Continuous Generation: Interaction Design for Writing with Generative Models on Mobile Devices Affect Text Length, Wording and Perceived Authorship

The AI Ghostwriter Effect: Users Do Not Perceive Ownership of AI-Generated Text But Self-Declare as Authors

“…Research on perceived authorship with generative AI is still scarce. Notably, Lehmann et al [72] could show a correlation of authorship with control in interaction design: In their study, perceived authorship was stronger when participants wrote their own texts than when they received text suggestions. In an interview study published in 2017, members of news organizations did not perceive algorithms as authors [77].…”

“…UI design factors) to emerging questions of authorship (cf. [72]). Indeed, many researchers (implicitly) touch upon such questions in their interaction studies, without addressing them explicitly in the study design, since that is not their focus: For instance, Lee et al [71] call their system "CoAuthor" and introduce measures of "mutuality" and "equality" of the text contributions by users and the AI.…”

“…To effectively design interactive LLMbased applications with authorship in mind, we have to know how different interaction methods affect authorship for personalized AI-generated texts. Lehmann et al [72] found that the sense of authorship is positively correlated with the degree of influence over the AI contribution. Thus, we also assess perceived control and leadership, and we hypothesize that H2.1: the degree of influence over the generated text, as realized by different interaction methods, affects the sense of control, and H2.2: the degree of influence over the generated text affects the sense of leadership.…”

“…To test H1 -H3 in Study 1, we varied the level of influence over the AI-generated texts in the postcard writing task by employing different Interaction Methods (within-subject factor). Based on the correlation of influence and sense of ownership suggested in [72,83], we defined four methods in descending order of the level of user influence: Writing, Editing, Choosing, and Getting. We generated texts with personalization or pseudo-personalization (fine-tuned vs.…”

“…One example of such collaboration can be seen in the use of language generation models, such as GPT-3, to assist with writing tasks. Previous HCI research has investigated user perspectives on collaborative creative writing [44,71,98], style transfer [87], text summarization [47], and perceived authorship with AI suggestions [72]. However, it has not yet investigated how people attribute and declare authorship for the generated text.…”