[{"author":[{"first_name":"Jan-Peter","full_name":"Kucklick, Jan-Peter","id":"77066","last_name":"Kucklick"}],"publication_status":"published","citation":{"short":"J.-P. Kucklick, Journal of Decision Systems (2023) 1–41.","mla":"Kucklick, Jan-Peter. “HIEF: A Holistic Interpretability and Explainability Framework.” <i>Journal of Decision Systems</i>, Taylor &#38; Francis, 2023, pp. 1–41, doi:<a href=\"https://doi.org/10.1080/12460125.2023.2207268\">10.1080/12460125.2023.2207268</a>.","bibtex":"@article{Kucklick_2023, title={HIEF: a holistic interpretability and explainability framework}, DOI={<a href=\"https://doi.org/10.1080/12460125.2023.2207268\">10.1080/12460125.2023.2207268</a>}, journal={Journal of Decision Systems}, publisher={Taylor &#38; Francis}, author={Kucklick, Jan-Peter}, year={2023}, pages={1–41} }","chicago":"Kucklick, Jan-Peter. “HIEF: A Holistic Interpretability and Explainability Framework.” <i>Journal of Decision Systems</i>, 2023, 1–41. <a href=\"https://doi.org/10.1080/12460125.2023.2207268\">https://doi.org/10.1080/12460125.2023.2207268</a>.","ieee":"J.-P. Kucklick, “HIEF: a holistic interpretability and explainability framework,” <i>Journal of Decision Systems</i>, pp. 1–41, 2023, doi: <a href=\"https://doi.org/10.1080/12460125.2023.2207268\">10.1080/12460125.2023.2207268</a>.","ama":"Kucklick J-P. HIEF: a holistic interpretability and explainability framework. <i>Journal of Decision Systems</i>. Published online 2023:1-41. doi:<a href=\"https://doi.org/10.1080/12460125.2023.2207268\">10.1080/12460125.2023.2207268</a>","apa":"Kucklick, J.-P. (2023). HIEF: a holistic interpretability and explainability framework. <i>Journal of Decision Systems</i>, 1–41. <a href=\"https://doi.org/10.1080/12460125.2023.2207268\">https://doi.org/10.1080/12460125.2023.2207268</a>"},"department":[{"_id":"195"},{"_id":"196"}],"publisher":"Taylor & Francis","date_created":"2023-05-26T05:04:45Z","status":"public","language":[{"iso":"eng"}],"publication_identifier":{"issn":["1246-0125","2116-7052"]},"year":"2023","_id":"45299","date_updated":"2023-05-26T05:08:36Z","title":"HIEF: a holistic interpretability and explainability framework","doi":"10.1080/12460125.2023.2207268","abstract":[{"lang":"eng","text":"Many applications are driven by Machine Learning (ML) today. While complex ML models lead to an accurate prediction, their inner decision-making is obfuscated. However, especially for high-stakes decisions, interpretability and explainability of the model are necessary. Therefore, we develop a holistic interpretability and explainability framework (HIEF) to objectively describe and evaluate an intelligent system’s explainable AI (XAI) capacities. This guides data scientists to create more transparent models. To evaluate our framework, we analyse 50 real estate appraisal papers to ensure the robustness of HIEF. Additionally, we identify six typical types of intelligent systems, so-called archetypes, which range from explanatory to predictive, and demonstrate how researchers can use the framework to identify blind-spot topics in their domain. Finally, regarding comprehensiveness, we used a random sample of six intelligent systems and conducted an applicability check to provide external validity."}],"keyword":["Explainable AI (XAI)","machine learning","interpretability","real estate appraisal","framework","taxonomy"],"main_file_link":[{"url":"https://www.tandfonline.com/doi/full/10.1080/12460125.2023.2207268"}],"user_id":"77066","publication":"Journal of Decision Systems","type":"journal_article","page":"1-41"},{"status":"public","year":"2022","type":"conference","language":[{"iso":"eng"}],"publication":"55th Annual Hawaii International Conference on System Sciences (HICSS-55)","date_created":"2021-11-17T07:08:15Z","date_updated":"2022-01-06T06:57:40Z","_id":"27506","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"}],"author":[{"last_name":"Kucklick","id":"77066","full_name":"Kucklick, Jan-Peter","first_name":"Jan-Peter"}],"conference":{"end_date":"2022-01-07","name":"Hawaii International Conference on System Science (HICSS)","location":"Virtual","start_date":"2022-01-03"},"title":"Visual Interpretability of Image-based Real Estate Appraisal","department":[{"_id":"195"},{"_id":"196"}],"oa":"1","user_id":"77066","keyword":["Explainable Artificial Intelligence (XAI)","Regression Activation Maps","Real Estate Appraisal","Convolutional Block Attention Module","Computer Vision"],"main_file_link":[{"open_access":"1","url":"https://scholarspace.manoa.hawaii.edu/bitstream/10125/79519/0149.pdf"}],"citation":{"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.","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.","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.","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."}},{"department":[{"_id":"195"}],"citation":{"apa":"Heuwinkel, T., Kucklick, J.-P., &#38; Müller, O. (2022). Using Geolocated Text to Quantify Location in Real Estate Appraisal. <i>55th Annual Hawaii International Conference on System Sciences (HICSS-55)</i>. Hawaii International Conference on System Science (HICSS), Virtual.","ama":"Heuwinkel T, Kucklick J-P, Müller O. Using Geolocated Text to Quantify Location in Real Estate Appraisal. In: <i>55th Annual Hawaii International Conference on System Sciences (HICSS-55)</i>. ; 2022.","chicago":"Heuwinkel, Tim, Jan-Peter Kucklick, and Oliver Müller. “Using Geolocated Text to Quantify Location in Real Estate Appraisal.” In <i>55th Annual Hawaii International Conference on System Sciences (HICSS-55)</i>, 2022.","ieee":"T. Heuwinkel, J.-P. Kucklick, and O. Müller, “Using Geolocated Text to Quantify Location in Real Estate Appraisal,” presented at the Hawaii International Conference on System Science (HICSS), Virtual, 2022.","mla":"Heuwinkel, Tim, et al. “Using Geolocated Text to Quantify Location in Real Estate Appraisal.” <i>55th Annual Hawaii International Conference on System Sciences (HICSS-55)</i>, 2022.","bibtex":"@inproceedings{Heuwinkel_Kucklick_Müller_2022, title={Using Geolocated Text to Quantify Location in Real Estate Appraisal}, booktitle={55th Annual Hawaii International Conference on System Sciences (HICSS-55)}, author={Heuwinkel, Tim and Kucklick, Jan-Peter and Müller, Oliver}, year={2022} }","short":"T. Heuwinkel, J.-P. Kucklick, O. Müller, in: 55th Annual Hawaii International Conference on System Sciences (HICSS-55), 2022."},"user_id":"77066","oa":"1","main_file_link":[{"open_access":"1","url":"https://scholarspace.manoa.hawaii.edu/bitstream/10125/80039/0561.pdf"}],"keyword":["Real Estate Appraisal","Text Regression","Natural Language Processing (NLP)","Location Intelligence","Wikipedia"],"abstract":[{"lang":"eng","text":"Accurate real estate appraisal is essential in decision making processes of financial institutions, governments, and trending real estate platforms like Zillow. One of the most important factors of a property’s value is its location. However, creating accurate quantifications of location remains a challenge. While traditional approaches rely on Geographical Information Systems (GIS), recently unstructured data in form of images was incorporated in the appraisal process, but text data remains an untapped reservoir. Our study shows that using text data in form of geolocated Wikipedia articles can increase predictive performance over traditional GIS-based methods by 8.2% in spatial out-of-sample validation. A framework to automatically extract geographically weighted vector representations for text is established and used alongside traditional structural housing features to make predictions and to uncover local patterns on sale price for real estate transactions between 2015 and 2020 in Allegheny County, Pennsylvania."}],"conference":{"location":"Virtual","start_date":"2022-01-03","name":"Hawaii International Conference on System Science (HICSS)","end_date":"2022-01-07"},"title":"Using Geolocated Text to Quantify Location in Real Estate Appraisal","author":[{"last_name":"Heuwinkel","first_name":"Tim","full_name":"Heuwinkel, Tim"},{"first_name":"Jan-Peter","full_name":"Kucklick, Jan-Peter","id":"77066","last_name":"Kucklick"},{"last_name":"Müller","id":"72849","first_name":"Oliver","full_name":"Müller, Oliver"}],"date_updated":"2022-01-06T06:57:40Z","_id":"27507","year":"2022","type":"conference","language":[{"iso":"eng"}],"status":"public","publication":"55th Annual Hawaii International Conference on System Sciences (HICSS-55)","date_created":"2021-11-17T07:12:03Z"},{"status":"public","language":[{"iso":"eng"}],"publication_identifier":{"issn":["2158-656X","2158-6578"]},"year":"2022","type":"journal_article","publisher":"Association for Computing Machinery (ACM)","date_created":"2023-01-10T05:16:02Z","publication":"ACM Transactions on Management Information Systems","date_updated":"2023-01-10T05:20:18Z","_id":"35620","abstract":[{"text":"Deep learning models fuel many modern decision support systems, because they typically provide high predictive performance. Among other domains, deep learning is used in real-estate appraisal, where it allows to extend the analysis from hard facts only (e.g., size, age) to also consider more implicit information about the location or appearance of houses in the form of image data. However, one downside of deep learning models is their intransparent mechanic of decision making, which leads to a trade-off between accuracy and interpretability. This limits their applicability for tasks where a justification of the decision is necessary. Therefore, in this paper, we first combine different perspectives on interpretability into a multi-dimensional framework for a socio-technical perspective on explainable artificial intelligence. Second, we measure the performance gains of using multi-view deep learning which leverages additional image data (satellite images) for real estate appraisal. Third, we propose and test a novel post-hoc explainability method called Grad-Ram. This modified version of Grad-Cam mitigates the intransparency of convolutional neural networks (CNNs) for predicting continuous outcome variables. With this, we try to reduce the accuracy-interpretability trade-off of multi-view deep learning models. Our proposed network architecture outperforms traditional hedonic regression models by 34% in terms of MAE. Furthermore, we find that the used satellite images are the second most important predictor after square feet in our model and that the network learns interpretable patterns about the neighborhood structure and density.","lang":"eng"}],"doi":"10.1145/3567430","author":[{"first_name":"Jan-Peter","full_name":"Kucklick, Jan-Peter","id":"77066","last_name":"Kucklick"},{"first_name":"Oliver","full_name":"Müller, Oliver","last_name":"Müller","id":"72849"}],"title":"Tackling the Accuracy–Interpretability Trade-off: Interpretable Deep Learning Models for Satellite Image-based Real Estate Appraisal","article_type":"original","department":[{"_id":"195"},{"_id":"196"}],"main_file_link":[{"url":"https://dl.acm.org/doi/pdf/10.1145/3567430"}],"keyword":["Interpretability","Convolutional Neural Network","Accuracy-Interpretability Trade-Of","Real Estate Appraisal","Hedonic Pricing","Grad-Ram"],"publication_status":"published","user_id":"77066","citation":{"bibtex":"@article{Kucklick_Müller_2022, title={Tackling the Accuracy–Interpretability Trade-off: Interpretable Deep Learning Models for Satellite Image-based Real Estate Appraisal}, DOI={<a href=\"https://doi.org/10.1145/3567430\">10.1145/3567430</a>}, journal={ACM Transactions on Management Information Systems}, publisher={Association for Computing Machinery (ACM)}, author={Kucklick, Jan-Peter and Müller, Oliver}, year={2022} }","mla":"Kucklick, Jan-Peter, and Oliver Müller. “Tackling the Accuracy–Interpretability Trade-off: Interpretable Deep Learning Models for Satellite Image-Based Real Estate Appraisal.” <i>ACM Transactions on Management Information Systems</i>, Association for Computing Machinery (ACM), 2022, doi:<a href=\"https://doi.org/10.1145/3567430\">10.1145/3567430</a>.","short":"J.-P. Kucklick, O. Müller, ACM Transactions on Management Information Systems (2022).","apa":"Kucklick, J.-P., &#38; Müller, O. (2022). Tackling the Accuracy–Interpretability Trade-off: Interpretable Deep Learning Models for Satellite Image-based Real Estate Appraisal. <i>ACM Transactions on Management Information Systems</i>. <a href=\"https://doi.org/10.1145/3567430\">https://doi.org/10.1145/3567430</a>","ama":"Kucklick J-P, Müller O. Tackling the Accuracy–Interpretability Trade-off: Interpretable Deep Learning Models for Satellite Image-based Real Estate Appraisal. <i>ACM Transactions on Management Information Systems</i>. Published online 2022. doi:<a href=\"https://doi.org/10.1145/3567430\">10.1145/3567430</a>","ieee":"J.-P. Kucklick and O. Müller, “Tackling the Accuracy–Interpretability Trade-off: Interpretable Deep Learning Models for Satellite Image-based Real Estate Appraisal,” <i>ACM Transactions on Management Information Systems</i>, 2022, doi: <a href=\"https://doi.org/10.1145/3567430\">10.1145/3567430</a>.","chicago":"Kucklick, Jan-Peter, and Oliver Müller. “Tackling the Accuracy–Interpretability Trade-off: Interpretable Deep Learning Models for Satellite Image-Based Real Estate Appraisal.” <i>ACM Transactions on Management Information Systems</i>, 2022. <a href=\"https://doi.org/10.1145/3567430\">https://doi.org/10.1145/3567430</a>."}}]