🚨New Pub at PNAS🚨! We know people cannot detect language written by AI. But what makes them THINK text was AI-generated? We show that people have consistent heuristics... that are flawed & can be exploited by AI to create text "more human than human" 🧵
https://www.pnas.org/doi/10.1073/pnas.2208839120
(apologies, rare cross-posting from Twitter -- in fact, rare posting on Twitter altogether).
In the first part of this work (lead author: Maurice Jakesch) we collected 1000s of human-written self-presentations in important contexts (dating, freelance, hospitality); created (#GPT) 1000s of AI-generated profiles; and asked 1000s of people to distinguish between them.
They couldn't (success rate: ~50%). However, they were consistent: people had specific ideas about which profile was AI/human. 2/
For example, use of rare bigrams & long words was associated with people thinking the profile was generated by AI. In reality, such profiles were more likely to be human-written. Use of informal speech and mentions of family were (wrongly) associated with human-written text.
We also ran an experiment. Since there are predictable features that make people think a profile sounds more "human", AI can take advantage of these features/heuristics to create profiles that are "more human than human".
Why is this important? It's now clear that more of our online content and communication will be generated by AI. In our previous work, we demonstrated the "Replicant Effect": as soon as the use of AI is suspected, evaluations of trustworthiness drop.
In the current work, we show that not only people cannot distinguish between AI and human-written text, but that they have heuristics that can be exploited by AI, potentially leaving the poor authentic humans to suffer decreased trustworthiness evaluations.
As we rush into deploying/using large language models and applications like #ChatGPT in our everyday communication, our paper asks to stop and consider the consequences as we move from Computer-mediated to AI-mediated communications (AI-MC).
The paper was a Cornell Tech & Stanford collaboration led by Maurice Jakesch (🚨on the job market in Europe🚨) and with Jeff Hancock. I will write about the origin of this NSF-funded research next. Also, more exciting AI-MC results to come soon!
Paper link again:
Neglected to add the open-access version. It's slightly older and not 100% the same text as the final PNAS paper but includes all the results/data.
Human communication is increasingly intermixed with language generated by AI. Across chat, email, and social media, AI systems suggest words, complete sentences, or produce entire conversations. AI-generated language is often not identified as such but presented as language written by humans, raising concerns about novel forms of deception and manipulation. Here, we study how humans discern whether verbal self-presentations, one of the most personal and consequential forms of language, were generated by AI. In six experiments, participants (N = 4,600) were unable to detect self-presentations generated by state-of-the-art AI language models in professional, hospitality, and dating contexts. A computational analysis of language features shows that human judgments of AI-generated language are hindered by intuitive but flawed heuristics such as associating first-person pronouns, use of contractions, or family topics with human-written language. We experimentally demonstrate that these heuristics make human judgment of AI-generated language predictable and manipulable, allowing AI systems to produce text perceived as "more human than human." We discuss solutions, such as AI accents, to reduce the deceptive potential of language generated by AI, limiting the subversion of human intuition.
@seachaint @x0r see open access here:
https://arxiv.org/abs/2206.07271
(not exactly the same text but very close)
Human communication is increasingly intermixed with language generated by AI. Across chat, email, and social media, AI systems suggest words, complete sentences, or produce entire conversations. AI-generated language is often not identified as such but presented as language written by humans, raising concerns about novel forms of deception and manipulation. Here, we study how humans discern whether verbal self-presentations, one of the most personal and consequential forms of language, were generated by AI. In six experiments, participants (N = 4,600) were unable to detect self-presentations generated by state-of-the-art AI language models in professional, hospitality, and dating contexts. A computational analysis of language features shows that human judgments of AI-generated language are hindered by intuitive but flawed heuristics such as associating first-person pronouns, use of contractions, or family topics with human-written language. We experimentally demonstrate that these heuristics make human judgment of AI-generated language predictable and manipulable, allowing AI systems to produce text perceived as "more human than human." We discuss solutions, such as AI accents, to reduce the deceptive potential of language generated by AI, limiting the subversion of human intuition.
Marianne Aubin Le Quéré
Mor Naaman
Jeffrey P. Bigham 🔥🔥
Ilya
x0r
Seachaint 