Location
Hilton Hawaiian Village, Honolulu, Hawaii
Event Website
https://hicss.hawaii.edu/
Start Date
3-1-2024 12:00 AM
End Date
6-1-2024 12:00 AM
Description
Voluntary knowledge contributions on question and answer (Q&A) platforms are important for users, platforms and organizations. Generative Artificial Intelligence (GAI) techniques have made it possible to automatically generate voluntary knowledge on Q&A platforms. The relative impact of GAI vs. humans on users' voluntary contribution of knowledge to Q&A platforms has yet to be explored. On the one hand, GAI can generate highly accurate answers because it is trained on large volumes of diverse, high-quality data. On the other hand, GAI can produce incorrect answers and fabricated facts. Using data from one of the largest Q&A platforms, Stack Overflow, we apply fixed effects models to understand the relative impact of GAI vs. human contributors on answer quality. We find that, on average, GAI answers receive lower scores and are shorter, but they can also be easier to read, more positive, and more objective. Our study has both theoretical and practical implications.
Recommended Citation
Shan, Guohou; Pienta, Dan; and Thatcher, Jason Bennet, "Investigating the Relative Impact of Generative AI vs. Humans on Voluntary Knowledge Contributions" (2024). Hawaii International Conference on System Sciences 2024 (HICSS-57). 2.
https://aisel.aisnet.org/hicss-57/st/research_and_education/2
Investigating the Relative Impact of Generative AI vs. Humans on Voluntary Knowledge Contributions
Hilton Hawaiian Village, Honolulu, Hawaii
Voluntary knowledge contributions on question and answer (Q&A) platforms are important for users, platforms and organizations. Generative Artificial Intelligence (GAI) techniques have made it possible to automatically generate voluntary knowledge on Q&A platforms. The relative impact of GAI vs. humans on users' voluntary contribution of knowledge to Q&A platforms has yet to be explored. On the one hand, GAI can generate highly accurate answers because it is trained on large volumes of diverse, high-quality data. On the other hand, GAI can produce incorrect answers and fabricated facts. Using data from one of the largest Q&A platforms, Stack Overflow, we apply fixed effects models to understand the relative impact of GAI vs. human contributors on answer quality. We find that, on average, GAI answers receive lower scores and are shorter, but they can also be easier to read, more positive, and more objective. Our study has both theoretical and practical implications.
https://aisel.aisnet.org/hicss-57/st/research_and_education/2