The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
Between Innovation and Ethical Challenges: The Impact of Artificial Intelligence in Social Science Research
Open accessAbstract
Artificial intelligence (AI) is increasingly influencing scientific research by improving the reliability and efficiency of literature reviews, data collection and analysis, as well as of academic writing. While AI offers significant advantages in automating repetitive tasks and streamlining research workflows, it also raises concerns about accuracy, transparency, and ethical integrity. This paper examines the dual impact of AI in social science research, highlighting its potential to improve efficiency while underlining the need for critical oversight of processes and outcomes. Experience to date suggests that researchers need to validate all AI-generated outputs and adopt a critical stance toward the use of AI in the creative stages of scientific endeavor. In addition, academic institutions should establish clear policies to regulate the use of AI, ensuring compliance with international standards and promoting ethical research practices. The future of AI in research depends on balancing the efficiency calculations supported by technological innovation with ethical responsibility and that of upholding the credibility and integrity of scientific knowledge.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“On the other hand, a laissez-faire approach may lead to the homogenization and superficialization of academic output, while exacerbating systemic inequities arising from inherent model biases (Hatos, 2025).”
From The Attention Mismatch: Mapping the Structural Academic Governance Deficit in the Age of Generative AI (2026) · cites this paper as Hatos, 2025
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
The publisher hasn’t deposited this paper’s reference list.
Research works printed but not linked: 15
- Bibliografi e Albaroudi, E., Mansouri, T., & Alameer, A. (2024). A comprehensive review of AI techniques for addressing algorithmic bias in job hiring, Ai, 5(1), 383-404.
- DiMaggio, P., & Hargittai, E. (2001). From the 'digital divide'to 'digital inequality': Studying Internet use as penetration increases, Princeton: Center for Arts and Cultural Policy Studies, Woodrow Wilson School, Princeton University, 4(1), 4-2.
- Huang, Y., Wu, R., He, J., & Xiang, Y. (2024). Evaluating ChatGPT-4.0's data analytic profi ciency in epidemiological studies: A comparative analysis with SAS, SPSS, and R, Journal of Global Health, 14, 04070.
- Hutson, M., & Mastin, A. (2023). Guinea pigbots, Science, 381(6654), 121-123.
- Jalali, M. S., & Akhavan, A. (2024). Integrating AI language models in qualitative research: Replicating interview data analysis with ChatGPT, System Dynamics Review, 40(3), e1772.
- Katsoulakis, E., Wang, Q., Wu, H., Shahriyari, L., Fletcher, R., Liu, J., Achenie, L., Liu, H., Jackson, P., & Xiao, Y. (2024). Digital twins for health: a scoping review, NPJ digital medicine, 7(1), 77.
- Konstantis, K., Georgas, A., Faras, A., Georgas, K., & Tympas, A. (2024). Ethical considerations in working with ChatGPT on a questionnaire about the future of work with ChatGPT, AI and Ethics, 4(4), 1335-1344.
- Nguyen-Trung, K. (2024). ChatGPT in Thematic Analysis: Can AI become a research assistant in qualitative research, OSF Preprint, researchgate. net.
- Picazo-Sanchez, P., & Ortiz-Martin, L. (2024). Analysing the impact of ChatGPT in research. Applied Intelligence, 54(5), 4172-4188.
- Pisica, A. I., Edu, T., Zaharia, R. M., & Zaharia, R. (2023). Implementing artifi cial intelligence in higher education: Pros and cons from the perspectives of academics, Societies, 13(5), 118.
- Selbst, A. D., Boyd, D., Friedler, S. A., Venkatasubramanian, S., & Vertesi, J. (2019). Fairness and abstraction in sociotechnical systems. Proceedings of the conference on fairness, accountability, and transparency. 59-68. Https://dl.acm.org/doi/ pdf/10.1145/3287560.3287598. Accesat în 16 martie 2025. · DOI
- Yildiz Durak, H., Eğin, F., & Onan, A. (2025). A Comparison of Human-Written Versus AI-Generated Text in Discussions at Educational Settings: Investigating Features for ChatGPT, Gemini and BingAI, European Journal of Education, 60(1), e70014.
- Yousaf, M. N. (2025). Practical Considerations and Ethical Implications of Using Artifi cial Intelligence in Writing Scientifi c Manuscripts, ACG Case Reports Journal 12(2), p e01629.
- Zarouali, B., Araujo, T., Ohme, J., & De Vreese, C. (2024). Comparing chatbots and online surveys for (longitudinal) data collection: an investigation of response characteristics, data quality, and user evaluation, Communication Methods and Measures, 18(1), 72-91.
- Zou, Z., Mubin, O., Alnajjar, F., & Ali, L. (2024). A pilot study of measuring emotional response and perception of LLM-generated questionnaire and human-generated questionnaires, Scientifi c Reports, 14(1), 2781.
Cited by
Sorted by citations
Guo, Z., Mao, H., & Zhang, F. (2026) · Publications · doi:10.3390/publications14020027
“On the other hand, a laissez-faire approach may lead to the homogenization and superficialization of academic output, while exacerbating systemic inequities arising from inherent model biases (Hatos, 2025).”
Metadata
- Authors
- Hatos, A.
- Affiliations
- University of Oradea (RO)
- Corresponding author
- Hatos, Adrian
- ORCID
- Hatos, Adrian
- Venue
- Sociologie Romaneasca
- Publisher
- Expert Projects
- Published
- 2025-06-15
- Volume
- 23
- Issue
- 1
- Pages
- 121-139
- ISSN
- 1220-5389 (ISSN-L)
- 2668-1455
- OpenAlex
- W4413617164
- Cited by
- 1
- References
- 0
- Citations by year
- 2026: 1
- FWCI
- 1.05
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- cc-by
- Locations
- Sociologie Romaneasca (publishedVersion, cc-by)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
- Social Sciences › General Social Sciences › Computational and Text Analysis Methods
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
- Keywords
- artificial intelligence in research, social science research, research ethics, AI-generated content, AI in academic writing, research integrity, scientific credibility
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
