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Internationalen Tagung Wirtschaftsinformatik 2022","file":[{"relation":"main_file","success":1,"content_type":"application/pdf","file_id":"27281","file_name":"WI2022_TaxonomyUse_Short_revise_v3.pdf","access_level":"closed","file_size":811007,"date_created":"2021-11-08T12:15:56Z","creator":"abursig","date_updated":"2021-11-08T12:15:56Z"}],"status":"public","user_id":"477","department":[{"_id":"276"}],"project":[{"_id":"1","name":"SFB 901"},{"_id":"4","name":"SFB 901 - Project Area C"},{"name":"SFB 901 - Subproject C5","_id":"17"}],"_id":"27280","language":[{"iso":"eng"}],"file_date_updated":"2021-11-08T12:15:56Z","ddc":["000"]},{"year":"2022","citation":{"bibtex":"@inproceedings{Altenhöner_Dieckmann_Münzmay_Richts-Matthaei_Röwenstrunk_Stellmacher_Pratschke_Primavesi_Schulz_2022, title={Kultur – Daten – Kuratierung: Was Speichern Wir Und Wozu?}, author={Altenhöner, Reinhard and Dieckmann, Lisa and Münzmay, Andreas and Richts-Matthaei, Kristina and Röwenstrunk, Daniel and Stellmacher, Martha and Pratschke, Margarete and Primavesi, Patrick and Schulz, Christoph}, year={2022} }","short":"R. Altenhöner, L. Dieckmann, A. Münzmay, K. Richts-Matthaei, D. Röwenstrunk, M. Stellmacher, M. Pratschke, P. Primavesi, C. Schulz, in: 2022.","mla":"Altenhöner, Reinhard, et al. <i>Kultur – Daten – Kuratierung: Was Speichern Wir Und Wozu?</i> 2022.","apa":"Altenhöner, R., Dieckmann, L., Münzmay, A., Richts-Matthaei, K., Röwenstrunk, D., Stellmacher, M., Pratschke, M., Primavesi, P., &#38; Schulz, C. (2022). <i>Kultur – Daten – Kuratierung: Was Speichern Wir Und Wozu?</i> Jahrestagung des Verbands Digital Humanities im deutschsprachigen Raum, Potsdam.","ieee":"R. Altenhöner <i>et al.</i>, “Kultur – Daten – Kuratierung: Was Speichern Wir Und Wozu?,” presented at the Jahrestagung des Verbands Digital Humanities im deutschsprachigen Raum, Potsdam, 2022.","chicago":"Altenhöner, Reinhard, Lisa Dieckmann, Andreas Münzmay, Kristina Richts-Matthaei, Daniel Röwenstrunk, Martha Stellmacher, Margarete Pratschke, Patrick Primavesi, and Christoph Schulz. “Kultur – Daten – Kuratierung: Was Speichern Wir Und Wozu?,” 2022.","ama":"Altenhöner R, Dieckmann L, Münzmay A, et al. Kultur – Daten – Kuratierung: Was Speichern Wir Und Wozu? In: ; 2022."},"date_updated":"2022-01-06T06:57:39Z","author":[{"last_name":"Altenhöner","full_name":"Altenhöner, Reinhard","first_name":"Reinhard"},{"first_name":"Lisa","last_name":"Dieckmann","full_name":"Dieckmann, Lisa"},{"first_name":"Andreas","full_name":"Münzmay, Andreas","last_name":"Münzmay"},{"first_name":"Kristina","last_name":"Richts-Matthaei","full_name":"Richts-Matthaei, Kristina"},{"first_name":"Daniel","full_name":"Röwenstrunk, Daniel","last_name":"Röwenstrunk"},{"full_name":"Stellmacher, Martha","last_name":"Stellmacher","first_name":"Martha"},{"first_name":"Margarete","full_name":"Pratschke, Margarete","last_name":"Pratschke"},{"last_name":"Primavesi","full_name":"Primavesi, Patrick","first_name":"Patrick"},{"full_name":"Schulz, Christoph","last_name":"Schulz","first_name":"Christoph"}],"date_created":"2021-11-15T13:33:14Z","title":"Kultur – Daten – Kuratierung: Was Speichern Wir Und Wozu?","conference":{"location":"Potsdam","end_date":"2022-03-11","start_date":"2022-03-07","name":"Jahrestagung des Verbands Digital Humanities im deutschsprachigen Raum"},"type":"conference","status":"public","_id":"27461","department":[{"_id":"233"}],"user_id":"439","language":[{"iso":"ger"}]},{"citation":{"ama":"Kucklick J-P. Visual Interpretability of Image-based Real Estate Appraisal. In: <i>55th Annual Hawaii International Conference on System Sciences (HICSS-55)</i>. ; 2022.","chicago":"Kucklick, Jan-Peter. “Visual Interpretability of Image-Based Real Estate Appraisal.” In <i>55th Annual Hawaii International Conference on System Sciences (HICSS-55)</i>, 2022.","ieee":"J.-P. Kucklick, “Visual Interpretability of Image-based Real Estate Appraisal,” presented at the Hawaii International Conference on System Science (HICSS), Virtual, 2022.","bibtex":"@inproceedings{Kucklick_2022, title={Visual Interpretability of Image-based Real Estate Appraisal}, booktitle={55th Annual Hawaii International Conference on System Sciences (HICSS-55)}, author={Kucklick, Jan-Peter}, year={2022} }","short":"J.-P. Kucklick, in: 55th Annual Hawaii International Conference on System Sciences (HICSS-55), 2022.","mla":"Kucklick, Jan-Peter. “Visual Interpretability of Image-Based Real Estate Appraisal.” <i>55th Annual Hawaii International Conference on System Sciences (HICSS-55)</i>, 2022.","apa":"Kucklick, J.-P. (2022). Visual Interpretability of Image-based Real Estate Appraisal. <i>55th Annual Hawaii International Conference on System Sciences (HICSS-55)</i>. Hawaii International Conference on System Science (HICSS), Virtual."},"year":"2022","date_created":"2021-11-17T07:08:15Z","author":[{"last_name":"Kucklick","full_name":"Kucklick, Jan-Peter","id":"77066","first_name":"Jan-Peter"}],"date_updated":"2022-01-06T06:57:40Z","oa":"1","main_file_link":[{"open_access":"1","url":"https://scholarspace.manoa.hawaii.edu/bitstream/10125/79519/0149.pdf"}],"conference":{"name":"Hawaii International Conference on System Science (HICSS)","start_date":"2022-01-03","end_date":"2022-01-07","location":"Virtual"},"title":"Visual Interpretability of Image-based Real Estate Appraisal","type":"conference","publication":"55th Annual Hawaii International Conference on System Sciences (HICSS-55)","status":"public","abstract":[{"text":"Explainability for machine learning gets more and more important in high-stakes decisions like real estate appraisal. While traditional hedonic house pricing models are fed with hard information based on housing attributes, recently also soft information has been incorporated to increase the predictive performance. This soft information can be extracted from image data by complex models like Convolutional Neural Networks (CNNs). However, these are intransparent which excludes their use for high-stakes financial decisions. To overcome this limitation, we examine if a two-stage modeling approach can provide explainability. We combine visual interpretability by Regression Activation Maps (RAM) for the CNN and a linear regression for the overall prediction. Our experiments are based on 62.000 family homes in Philadelphia and the results indicate that the CNN learns aspects related to vegetation and quality aspects of the house from exterior images, improving the predictive accuracy of real estate appraisal by up to 5.4%.","lang":"eng"}],"user_id":"77066","department":[{"_id":"195"},{"_id":"196"}],"_id":"27506","language":[{"iso":"eng"}],"keyword":["Explainable Artificial Intelligence (XAI)","Regression Activation Maps","Real Estate Appraisal","Convolutional Block Attention Module","Computer Vision"]}]