Towards the Quantitative Interpretability Analysis of Citizens Happiness Prediction

Li, Lin and Wu, Xiaohua and Kong, Miao and Zhou, Dong and Tao, Xiaohui ORCID: https://orcid.org/0000-0002-0020-077X (2022) Towards the Quantitative Interpretability Analysis of Citizens Happiness Prediction. In: 31st International Joint Conference on Artificial Intelligence (IJCAI-ECAI 2022): Special Track on AI for Good, 23 July - 29 July 2022, Vienna, Austria.


Abstract

Evaluating the high-effect factors of citizens' happiness is beneficial to a wide range of policy-making for economics and politics in most countries. Benefiting from the high-efficiency of regression models, previous efforts by sociology scholars have analyzed the effect of happiness factors with high interpretability. However, restricted to their research concerns, they are specifically interested in some subset of factors modeled as linear functions. Recently, deep learning shows promising prediction accuracy while addressing challenges in interpretability. To this end, we introduce Shapley value that is inherent in solid theory for factor contribution interpretability to work with deep learning models by taking into account interactions between multiple factors. The proposed solution computes the Shapley value of a factor, i.e., its average contribution to the prediction in different coalitions based on coalitional game theory. Aiming to evaluate the interpretability quality of our solution, experiments are conducted on a Chinese General Social Survey (CGSS) questionnaire dataset. Through systematic reviews, the experimental results of Shapley value are highly consistent with academic studies in social science, which implies our solution for citizens' happiness prediction has 2-fold implications, theoretically and practically.


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Item Type: Conference or Workshop Item (Commonwealth Reporting Category E) (Paper)
Refereed: Yes
Item Status: Live Archive
Additional Information: Files associated with this item cannot be displayed due to copyright restrictions.
Faculty/School / Institute/Centre: Current – Faculty of Health, Engineering and Sciences - School of Mathematics, Physics and Computing (1 Jan 2022 -)
Faculty/School / Institute/Centre: Current – Faculty of Health, Engineering and Sciences - School of Mathematics, Physics and Computing (1 Jan 2022 -)
Date Deposited: 09 Aug 2022 03:47
Last Modified: 07 Nov 2022 00:52
Uncontrolled Keywords: Humans and AI: Computational Sustainability and Human Well-Being AI Ethics, Trust, Fairness: Explainability and Interpretability AI Ethics, Trust, Fairness: Societal Impact of AI Machine Learning: Explainable/Interpretable Machine Learning
Fields of Research (2020): 46 INFORMATION AND COMPUTING SCIENCES > 4608 Human-centred computing > 460899 Human-centred computing not elsewhere classified
46 INFORMATION AND COMPUTING SCIENCES > 4602 Artificial intelligence > 460208 Natural language processing
46 INFORMATION AND COMPUTING SCIENCES > 4610 Library and information studies > 461003 Human information interaction and retrieval
Socio-Economic Objectives (2020): 28 EXPANDING KNOWLEDGE > 2801 Expanding knowledge > 280115 Expanding knowledge in the information and computing sciences
22 INFORMATION AND COMMUNICATION SERVICES > 2204 Information systems, technologies and services > 220403 Artificial intelligence
Funding Details:
URI: http://eprints.usq.edu.au/id/eprint/50818

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