Can AI really replace human data? The evidence says no.
The short answer is no—at least not for the kind of rich, qualitative data that captures human experience. In a 2024 story completion study on mobile dating, researchers unexpectedly encountered AI-generated responses in their dataset. Using feminist new materialism as a theoretical lens, they found that AI responses lacked the affective and discursive qualities of human-generated data—the AI simply could not replicate the 'richness of human experience' central to qualitative research [1]. This wasn't a subtle difference; the researchers could identify AI responses by their absence of emotional depth and narrative authenticity.
Similarly, a 2023 experiment on dishonesty found that AI advice (generated by a natural language processing algorithm) influenced behavior in the same way as human advice—but only when promoting dishonesty. Honesty-promoting advice from either source did not increase honesty [6]. This suggests AI can mimic some human influences but fails to capture the full spectrum of human moral reasoning. The implication for data value is clear: if you need data that reflects genuine human affect, ethical nuance, or lived experience, AI-generated substitutes fall short.
Why human values make human data uniquely valuable.
Human decision-making is shaped by pluralistic values—multiple, sometimes conflicting values like honesty versus friendship. AI systems, as statistical learners, tend to average out these conflicts, washing away the very tensions that make human data rich. A 2024 study introduced ValuePrism, a dataset of 218,000 values, rights, and duties connected to 31,000 human-written situations. The AI (GPT-4) generated these values, and human annotators deemed them high-quality 91% of the time [4]. But here's the catch: when the researchers built a model (Kaleido) that explicitly modeled value pluralism, humans actually preferred Kaleido's outputs over GPT-4's, finding them more accurate and with broader coverage [4]. This means that even when AI generates seemingly high-quality data, it still misses the mark on representing the full range of human values—making human-generated data that captures those tensions more valuable, not less.
Another 2024 paper on software engineering research argued that while AI can emulate humans in interviews and surveys, an 'integrated approach where both AI and human-generated data coexist will likely yield the most effective outcomes' [2]. This is not a dismissal of AI, but a recognition that human data provides something AI cannot: the authentic, context-rich perspective that is 'fundamentally required' in sociotechnical domains [2].
The demand for human data is rising, not falling.
Ironically, the AI era is increasing the demand for high-quality human data. A 2022 policy paper on healthcare data noted that 'healthcare AI technologies rely on data in enhancing their scope,' and that a lack of data 'hinders the creation of future applications' [3]. The authors argued for better incentives to encourage patients to share their data, precisely because AI systems need vast amounts of high-quality human data to improve. This is not a niche concern—healthcare AI is one of the most data-hungry fields, and the paper highlights that current data-sharing restrictions are a bottleneck.
Even in pedagogy, a 2024 paper on religious studies suggested that AI and human experts could 'mutually inspire, enrich, and even catechize one another' [5]. The authors framed this as a collaboration where human expertise—including intellectual virtuosity, not just rule-based reasoning—remains central. The takeaway is consistent across domains: AI does not replace the need for human data; it amplifies it, because AI systems are trained on human data and their outputs are only as good as that training data.
About These Sources
This answer is built on 6 peer-reviewed studies — published from 2022 to 2024, 4 from 2024 or later, 3 in Q1 journals, collectively cited 156 times — selected as the most relevant from 6 studies that passed quality screening, drawn from 62 papers retrieved from a database of over 500 million.
Sources used in this answer
More or less than human? Evaluating the role of AI-as-participant in online qualitative research
In a story completion study on mobile dating, AI-generated responses were identified by their lack of affective and discursive qualities compared to human data; the study concluded AI cannot replicate the richness of human experience [1].
Can AI serve as a substitute for human subjects in software engineering research?
This vision paper on software engineering research argues that while AI can emulate humans in qualitative studies, an integrated approach with both AI and human data will yield the most effective outcomes [2].
Incentivizing the sharing of healthcare data in the AI Era
A policy paper on healthcare data argues that AI technologies rely on human data to improve, and that current data-sharing restrictions hinder AI development, calling for better incentives to share data [3].
Value Kaleidoscope: Engaging AI with Pluralistic Human Values, Rights, and Duties
Using a dataset of 218,000 values from 31,000 situations, the study found that a model explicitly representing value pluralism (Kaleido) was preferred by humans over GPT-4 for accuracy and coverage, showing AI struggles with pluralistic human values [4].
Attention (to Virtuosity) Is All You Need: Religious Studies Pedagogy and Generative AI
A pedagogy paper suggests AI and human experts can collaborate in education, with human intellectual virtuosity remaining central and AI serving as a complementary tool [5].
Corrupted by Algorithms? How AI-generated and Human-written Advice Shape (Dis)honesty
In an experiment, AI advice promoting dishonesty increased dishonest behavior similarly to human advice, but honesty-promoting advice from either source did not increase honesty; algorithmic transparency did not affect behavior [6].