Explainable dimensionality reduction (XDR) to unbox AI ‘black box’ models: A study of AI perspectives on the ethnic styles of village dwellings
Abstract Artificial intelligence (AI) has become frequently used in data and knowledge production in diverse domain studies. Scholars began to reflect on the plausibility of AI models that learn unexplained tacit knowledge, spawning the emerging research field, eXplainable AI (XAI). However, superio...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Springer Nature
2023-01-01
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| Series: | Humanities & Social Sciences Communications |
| Online Access: | https://doi.org/10.1057/s41599-023-01505-4 |
| _version_ | 1811175943994081280 |
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| author | Xun Li Dongsheng Chen Weipan Xu Haohui Chen Junjun Li Fan Mo |
| author_facet | Xun Li Dongsheng Chen Weipan Xu Haohui Chen Junjun Li Fan Mo |
| author_sort | Xun Li |
| collection | DOAJ |
| description | Abstract Artificial intelligence (AI) has become frequently used in data and knowledge production in diverse domain studies. Scholars began to reflect on the plausibility of AI models that learn unexplained tacit knowledge, spawning the emerging research field, eXplainable AI (XAI). However, superior XAI approaches have yet to emerge that can explain the tacit knowledge acquired by AI models into human-understandable explicit knowledge. This paper proposes a novel eXplainable Dimensionality Reduction (XDR) framework, which aims to effectively translate the high-dimensional tacit knowledge learned by AI into explicit knowledge that is understandable to domain experts. We present a case study of recognizing the ethnic styles of village dwellings in Guangdong, China, via an AI model that can recognize the building footprints from satellite imagery. We find that the patio, size, length, direction and asymmetric shape of the village dwellings are the key to distinguish Canton, Hakka, Teochew or their mixed styles. The data-derived results, including key features, proximity relationships and geographical distribution of the styles are consistent with the findings of existing field studies. Moreover, an evidence of Hakka migration was also found in our results, complementing existing knowledge in architectural and historical geography. This proposed XDR framework can assist experts in diverse fields to further expand their domain knowledge. |
| first_indexed | 2024-04-10T19:44:12Z |
| format | Article |
| id | doaj.art-7c32cb7e4707405e9688446abc3eb777 |
| institution | Directory Open Access Journal |
| issn | 2662-9992 |
| language | English |
| last_indexed | 2024-04-10T19:44:12Z |
| publishDate | 2023-01-01 |
| publisher | Springer Nature |
| record_format | Article |
| series | Humanities & Social Sciences Communications |
| spelling | doaj.art-7c32cb7e4707405e9688446abc3eb7772023-01-29T12:06:55ZengSpringer NatureHumanities & Social Sciences Communications2662-99922023-01-0110111310.1057/s41599-023-01505-4Explainable dimensionality reduction (XDR) to unbox AI ‘black box’ models: A study of AI perspectives on the ethnic styles of village dwellingsXun Li0Dongsheng Chen1Weipan Xu2Haohui Chen3Junjun Li4Fan Mo5Sun Yat-sen UniversityTechnical University of MunichSun Yat-sen UniversityCommonwealth Scientific and Industrial Research OrganisationSun Yat-sen UniversitySun Yat-sen UniversityAbstract Artificial intelligence (AI) has become frequently used in data and knowledge production in diverse domain studies. Scholars began to reflect on the plausibility of AI models that learn unexplained tacit knowledge, spawning the emerging research field, eXplainable AI (XAI). However, superior XAI approaches have yet to emerge that can explain the tacit knowledge acquired by AI models into human-understandable explicit knowledge. This paper proposes a novel eXplainable Dimensionality Reduction (XDR) framework, which aims to effectively translate the high-dimensional tacit knowledge learned by AI into explicit knowledge that is understandable to domain experts. We present a case study of recognizing the ethnic styles of village dwellings in Guangdong, China, via an AI model that can recognize the building footprints from satellite imagery. We find that the patio, size, length, direction and asymmetric shape of the village dwellings are the key to distinguish Canton, Hakka, Teochew or their mixed styles. The data-derived results, including key features, proximity relationships and geographical distribution of the styles are consistent with the findings of existing field studies. Moreover, an evidence of Hakka migration was also found in our results, complementing existing knowledge in architectural and historical geography. This proposed XDR framework can assist experts in diverse fields to further expand their domain knowledge.https://doi.org/10.1057/s41599-023-01505-4 |
| spellingShingle | Xun Li Dongsheng Chen Weipan Xu Haohui Chen Junjun Li Fan Mo Explainable dimensionality reduction (XDR) to unbox AI ‘black box’ models: A study of AI perspectives on the ethnic styles of village dwellings Humanities & Social Sciences Communications |
| title | Explainable dimensionality reduction (XDR) to unbox AI ‘black box’ models: A study of AI perspectives on the ethnic styles of village dwellings |
| title_full | Explainable dimensionality reduction (XDR) to unbox AI ‘black box’ models: A study of AI perspectives on the ethnic styles of village dwellings |
| title_fullStr | Explainable dimensionality reduction (XDR) to unbox AI ‘black box’ models: A study of AI perspectives on the ethnic styles of village dwellings |
| title_full_unstemmed | Explainable dimensionality reduction (XDR) to unbox AI ‘black box’ models: A study of AI perspectives on the ethnic styles of village dwellings |
| title_short | Explainable dimensionality reduction (XDR) to unbox AI ‘black box’ models: A study of AI perspectives on the ethnic styles of village dwellings |
| title_sort | explainable dimensionality reduction xdr to unbox ai black box models a study of ai perspectives on the ethnic styles of village dwellings |
| url | https://doi.org/10.1057/s41599-023-01505-4 |
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