\u201cAs people continue to collaborate with AI, both on an individual but also on a team level, it becomes increasingly unclear who is contributing to the work and who owns the work,\u201d Riedl told Northeastern Global News.\u00a0<\/p>\n
What happens when large language models (LLMs) have a much smaller hand in the final product? How do you acknowledge their use while making sure that human collaborators get credit for their contributions? And how do you improve the collaboration itself, making sure AI doesn\u2019t iron out potentially important wrinkles in the argument.<\/p>\n
That\u2019s where it starts to get messy, Riedl said.<\/p>\n
Together with Khoury College of Computer Sciences professor Saiph Savage, computer sciences Ph.D. student Kashif Imteyaz<\/a> and other colleagues, Riedl published a project proposal<\/a> that suggests a novel way to probe for answers \u2014 a workshop that will serve as a real-world testing sandbox for exploring and potentially solving problems that arise in human-AI collaboration.\u00a0<\/p>\n The event, which will take place in Salt Lake City, Utah, this October, will bring together a multidisciplinary group of researchers and practitioners to explore the dynamics of human-AI collaboration and brainstorm ways to fairly account for contributions, preserve a chain of responsibility and nurture creativity when teaming up with bots.<\/p>\n \u201cLet\u2019s just experience it to study it,\u201d Riedl said.<\/p>\n And there\u2019s plenty to experience and study. AI agents are increasingly baked into collaborative workflows. For example, the transcription software Otter.ai might turn a roomful of people\u2019s comments into one neat meeting summary.<\/p>\n They\u2019re still fixtures of individual work, too. Tools such as Jenni AI or SciSpace draft outlines and write explanations for computational results. Claude composes code. And there\u2019s always ChatGPT for bouncing off ideas, polishing drafts or tracking down that molecule name that\u2019s been on the tip of your tongue for the last 20 minutes.<\/p>\n It\u2019s easy to miss how much editorial input bots have on content people write, share and eventually publish in scientific journals, Imteyaz said.<\/p>\n Institutions are trying to meet the moment. Back in 2023, the journal Nature<\/a> called for scientists using bots as research assistants to disclose their use in methods or acknowledgment sections. Companies such as Wiley<\/a> and Elsevier have made<\/a> similar requests, sometimes asking researchers to include their prompts. And Anthropic recently announced that Claude models launched this month and later will include a watermark<\/a> tagging text and images as AI-generated, although the method isn\u2019t foolproof.\u00a0<\/p>\n For example, false positives are possible if the user\u2019s language matches the bot\u2019s. At the same time, the lack of a watermark won\u2019t necessarily mean that AI had no hand in the manuscript.<\/p>\n In the absence of a watermark, voluntary disclosure might seem like a simple enough ask. Except AI use isn\u2019t nearly that tidy.\u00a0<\/p>\n \u201cYou have some initial idea, you give it to ChatGPT. You co-think, co-iterate,\u201d Imteyaz said. \u201cIt\u2019s very hard to distinguish what is yours \u2026 and what is the whisper from the agent,\u201d he added. And disclosures can\u2019t capture contributions you never recognized as being AI-influenced in the first place, he explained.\u00a0<\/p>\n And asking researchers to include prompts simply doesn\u2019t make sense, Riedl added. An interaction often amounts to a \u201cmulti-day conversation built on memories that the chatbot forms,\u201d he said. The prompt only captures a sliver of the exchange and isn\u2019t enough to recreate the response.\u00a0<\/p>\n With several people bringing bot-influenced ideas to the table, things get even murkier, Riedl said. Each person arrives without knowing exactly how much of an idea is their own. The group\u2019s collective use of AI adds another layer of uncertainty.\u00a0<\/p>\n For example, while \u201ccleaning up\u201d meeting notes, AI might \u201crestructure the argument in ways that redistribute credit for key ideas,\u201d the researchers argue. They call the result \u201ccontribution dissolution\u201d \u2014 a kind of authorship fog that can sabotage healthy collaboration from the get-go.<\/p>\n But credit isn\u2019t the only thing at stake \u2014 it\u2019s also the originality of the ideas themselves, Riedl said.<\/p>\n
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