Human Bias in the Face of AI : Examining Human Judgment Against Text Labeled as AI Generated (2025)

Abstract: As Al advances in text generation, human trust in Al generated content remains constrained by biases that go beyond concerns of accuracy. This study explores how bias shapes the perception of AI versus human generated content. Through three experiments involving text rephrasing, news article summarization, and persuasive writing, we investigated how human raters respond to labeled and unlabeled content. While the raters could not differentiate the two types of texts in the blind test, they overwhelmingly favored content labeled as “Human Generated,” over those labeled “AI Generated,” by a preference score of over 30%. We observed the same pattern even when the labels were deliberately swapped. This human bias against AI has broader societal and cognitive implications, as it undervalues AI performance. This study highlights the limitations of human judgment in interacting with AI and offers a foundation for improving human-AI collaboration, especially in creative fields.

## introduction with the increasing accessibility of large language models (llms), such as openai's chatgpt @xcite ), meta's llama @xcite , and anthropic's claude @xcite , generative artificial intelligence(ai)'s capabilities are being utilized in expansive tasks. their sophisticated text generation abilities raise both excitement and concern about the potential displacement of human workers @xcite . public confidence in ai is critical for its successful integration into society. positive perception of ai generated content helps forge a collaborative and productive relationship between humans and ai. without this trust, the use of llms could face significant resistance, leading to underutilization @xcite . prior research on ai mistrust has focused primarily on ai's bias towards different human pop-figure 1 : without labels for the texts, raters have little preference for ai generated or human written texts. with labels, raters prefer texts labeled "human generated" even when the labels were purposely swapped. our work connects to interdisciplinary research on human-computer algorithm interaction. a study found that algorithm aversion depends on task type, with humans resisting ai in subjective tasks @xcite . another earlier study showed people avoid algorithms after seeing them make even minor errors @xcite . more relevant to our approach, "human favoritism" was observed when evaluators knew content sources @xcite . similarly, trust in humans versus chatgpt was explored, focusing on explicit ratings @xcite . our study advances this through controlled blind tests and label-swapping experiments that more conclusively demonstrate bias against ai content. since their invention, humans have accepted computers for processing logical and mathematical tasks. chatgpt 3.5's release in 2022 and subsequent ai improvements raised alarm regarding humans' perceived intellectual superiority in creativity and language processing @xcite . past studies found that people view ai as inferior in work requiring originality @xcite . however, whether the belief that ai cannot match human skills in creative work like "writing" is rooted in reality or an expression of human bias requires further study. this research aims to uncover whether biases distort judgments in writing. we focus on three scenarios that have common natural language processing applications: text rephrasing, where ai enhances the readability of existing text @xcite ; summarization, where ai condenses longer texts into concise forms @xcite ; and persuasion, where ai argues for a belief @xcite . we summarize our contributions in three folds: ## results taskers fail to differentiate between human and ai generated texts. in the blind-labeled experiments, taskers were asked to guess which text was written by human. we found that, for the text rephrasing scenario, 49.93% taskers responded incorrectly, i.e. they believed the ai rephrased texts were human generated. for the summarization scenario, the average was lower at 43.1% responding incorrectly, but in the persuasion scenario, we again got a near-equal split of 50.06%. in blind tests, taskers show significant bias. we conducted a chi-squared test @xcite to identify if taskers have bias toward the text they prefer with four categories (text 1 is labeled ai and preferred; text 2 is labeled ai and preferred; 1 is labeled ai and 2 is preferred; and vice versa). our chi-squared score was 28.4 with a p-value <.00001, so we determined there was bias among which text people prefer when no label is given. across all scenarios, taskers slightly preferred ai-generated text in blind-labeled experiments, with ai texts scoring above the 1.50 midpoint in all cases-1.537 for rephrasing, 1.650 for summarization, and 1.534 for persuasion. taskers prefer text labeled "human generated" even when wrongly classified. compared to blind tests, scores for humangenerated texts in correctly labeled experiments increased by 32.9% (rephrasing), 35.1% (summarization), and 26.1% (persuasion), showing that the taskers strongly prefer "human generated" texts. in the wrongly labeled experiments, the scores for the texts labeled "human generated," but are actually "ai generated," also increased compared to the blind-labeled cases. for the text rephrasing scenario, the score for ai texts increased from 1.537 (51.2%) to 2.052 (68.4%) when they were labeled as "human generated." consequently, the score for human texts dropped from 1.463 (48.8%) to 0.948 (31.6%). similar results happened for the summarization and persuasion scenarios as well, with drops of 47.8% and 36.8%, respectively. appendix b shows an example task from each scenario where human bias played a large role and all three taskers preferred the human text when correctly labeled as "human generated" and the ai text when wrongly labeled as "human generated." the brunner-munzel test confirmed significant differences between all labeling conditions (@xmath0.05) @xcite . ## discussion and conclusion in our tests, taskers showed nearly equal ability in distinguishing between human and ai-generated text, consistent with prior research finding that linguists could only correctly identify ai-generated academic writing 38.9% of the time (casal and kessler, 2023). our results extend these findings to three other scenarios as well, suggesting that the challenge of differentiation remains true for all types of writing. our study also found that, when explicitly labeled, people prefer "human generated" texts more than "ai generated." one possible reason for the preference change is that people may mistrust llms due to their tendency to "hallucinate," outputting inaccurate or misleading information. although this might be the case for the persuasion scenario, where llms have greater generative creativity for argumentative writing @xcite , hallucinations are unlikely to occur in the rephrasing and summarization scenarios. for all scenarios, we manually examined a subset of output samples from each llm and did not notice hallucinations. this finding, supported by the significant p-values, provides preliminary evidence that human bias against ai persists regardless of task complexity. these biases hinder ai deployment and could impact ai alignment in systems that use reinforcement learning from human feedback (rlhf). rhlf uses human feedback to refine models like instructgpt @xcite , but if humans favor content perceived as human-generated, ai systems may be trained to produce content aligning with these biased expectations. for practitioners implementing nlp systems, we propose a few solutions to increase human trust in ai: while our research identified human bias as a major factor against ai content, we still need to pinpoint the source of human bias. we suspect "human ego" plays a role. humans are not ready to give up creative and intellectual superiority to ai, and therefore do not view ai content favorably. this hypothesis requires further investigation.