The ‘Implicit Intelligence’ of artificial intelligence. Investigating the potential of large language models in social science research
Researchers in ‘hard' science disciplines are exploring the transformative potential of Artificial Intelligence (AI) for advancing research in their fields. Their colleagues in ‘soft' science, however, have produced thus far a limited number of articles on this subject. This paper addresse...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2024-12-01
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| Series: | Political Research Exchange |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/2474736X.2024.2351794 |
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| author | Ottorino Cappelli Marco Aliberti Rodrigo Praino |
| author_facet | Ottorino Cappelli Marco Aliberti Rodrigo Praino |
| author_sort | Ottorino Cappelli |
| collection | DOAJ |
| description | Researchers in ‘hard' science disciplines are exploring the transformative potential of Artificial Intelligence (AI) for advancing research in their fields. Their colleagues in ‘soft' science, however, have produced thus far a limited number of articles on this subject. This paper addresses this gap. Our main hypothesis is that existing Artificial Intelligence Large Language Models (LLMs) can closely align with human expert assessments in specialized social science surveys. To test this, we compare data from a multi-country expert survey with those collected from the two powerful LLMs created by OpenAI and Google. The statistical difference between the two sets of data is minimal in most cases, supporting our hypothesis, albeit with certain limitations and within specific parameters. The tested language models demonstrate domain-agnostic algorithmic accuracy, indicating an inherent ability to incorporate human knowledge and independently replicate human judgment across various subfields without specific training. We refer to this property as the ‘implicit intelligence' of Artificial Intelligence, representing a highly promising advancement for social science research. |
| format | Article |
| id | doaj-art-3e780c98b46b4c5ea3bf61e5f3a1bcaa |
| institution | Kabale University |
| issn | 2474-736X |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Political Research Exchange |
| spelling | doaj-art-3e780c98b46b4c5ea3bf61e5f3a1bcaa2024-12-24T11:33:41ZengTaylor & Francis GroupPolitical Research Exchange2474-736X2024-12-016110.1080/2474736X.2024.2351794The ‘Implicit Intelligence’ of artificial intelligence. Investigating the potential of large language models in social science researchOttorino Cappelli0Marco Aliberti1Rodrigo Praino2Dipartimento di Scienze Sociali, Università degli Studi di Napoli “L’Orientale”, Naples, ItalyEuropean Space Policy Institute, Wien, AustriaJeff Bleich Centre for Democracy and Disruptive Technologies, Flinders University, Adelaide, AustraliaResearchers in ‘hard' science disciplines are exploring the transformative potential of Artificial Intelligence (AI) for advancing research in their fields. Their colleagues in ‘soft' science, however, have produced thus far a limited number of articles on this subject. This paper addresses this gap. Our main hypothesis is that existing Artificial Intelligence Large Language Models (LLMs) can closely align with human expert assessments in specialized social science surveys. To test this, we compare data from a multi-country expert survey with those collected from the two powerful LLMs created by OpenAI and Google. The statistical difference between the two sets of data is minimal in most cases, supporting our hypothesis, albeit with certain limitations and within specific parameters. The tested language models demonstrate domain-agnostic algorithmic accuracy, indicating an inherent ability to incorporate human knowledge and independently replicate human judgment across various subfields without specific training. We refer to this property as the ‘implicit intelligence' of Artificial Intelligence, representing a highly promising advancement for social science research.https://www.tandfonline.com/doi/10.1080/2474736X.2024.2351794Artificial intelligencepolitical science researchlarge language modelsspace policyspace power |
| spellingShingle | Ottorino Cappelli Marco Aliberti Rodrigo Praino The ‘Implicit Intelligence’ of artificial intelligence. Investigating the potential of large language models in social science research Political Research Exchange Artificial intelligence political science research large language models space policy space power |
| title | The ‘Implicit Intelligence’ of artificial intelligence. Investigating the potential of large language models in social science research |
| title_full | The ‘Implicit Intelligence’ of artificial intelligence. Investigating the potential of large language models in social science research |
| title_fullStr | The ‘Implicit Intelligence’ of artificial intelligence. Investigating the potential of large language models in social science research |
| title_full_unstemmed | The ‘Implicit Intelligence’ of artificial intelligence. Investigating the potential of large language models in social science research |
| title_short | The ‘Implicit Intelligence’ of artificial intelligence. Investigating the potential of large language models in social science research |
| title_sort | implicit intelligence of artificial intelligence investigating the potential of large language models in social science research |
| topic | Artificial intelligence political science research large language models space policy space power |
| url | https://www.tandfonline.com/doi/10.1080/2474736X.2024.2351794 |
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