Abstract
This paper presents a reduced version of a currently in-use questionnaire, the GENCAT scale, to determine the level of quality of life of people with intellectual disability and uses a framework in the literature of neurosymbolic AI, specifically the family of interpretable DL named logic explained networks, to provide explanations for the predictions. By integrating explainability, our research enhances the richness of the predictions and qualitatively evaluates the reduced questionnaire’s effectiveness, also illustrating the importance of explainable AI in improving assessment tools for vulnerable populations such as people with disability. The work is understood as a step to initiate dialogue with experts and practitioners using the GENCAT scale, with the ultimate goal of refining the questionnaire by discussing the explanations generated.
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Acknowledgement
This work was funded by the projects PID2022-139835NB-C21, H2020-MSCA-RISE-910 2020 MOSAIC, and PID2022-141950OB-I00, the groups 2021-SGR-00754 and 2021-SGR-00517, and the introduction to research training programme JAE Intro ICU 2023 (AIHUB-22, JAEICU_23_00654). We would like to thank the reviewers for their thoughtful comments, which enhanced the manuscript and will serve to improve our future research.
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5 Appendix
5 Appendix
The output of the computation for obtaining all the data of the social service users from the IntDisCat database, i.e., the standard values of the eight QOL dimensions and the QOL levels with its qualitative evaluation
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Fraile-Parra, D., Costa, V., Dellunde, P. (2024). LENs for Analyzing the Quality of Life of People with Intellectual Disability. In: Besold, T.R., d’Avila Garcez, A., Jimenez-Ruiz, E., Confalonieri, R., Madhyastha, P., Wagner, B. (eds) Neural-Symbolic Learning and Reasoning. NeSy 2024. Lecture Notes in Computer Science(), vol 14980. Springer, Cham. https://doi.org/10.1007/978-3-031-71170-1_15
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