Fetching the paper…
Reading the bibliography…
The use of eXplainable Artificial Intelligence (XAI) systems has introduced a set of challenges that need resolution.
Interpretable machine learning: definitions, methods, and applications
Murdoch, W. J., Singh, C., Kumbier, K., Abbasi-Asl, R., and Yu, B. (2019) · 1901
Earlier work this paper cites.
Classification and regression trees
Breiman, L. (1984) · 1984
Earlier work this paper cites.
Statistics (international student edition)
Freedman, D., Pisani, R., and Purves, R. (2007) · 2007
Earlier work this paper cites.
Scikit-learn: Machine learning in Python
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., and Duchesnay, E. (2011) · 2011
Earlier work this paper cites.
Describing textures in the wild
Cimpoi, M., Maji, S., Kokkinos, I., Mohamed, S., , and Vedaldi, A. (2014) · 2014
Earlier work this paper cites.
On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation
Bach, S., Binder, A., Montavon, G., Klauschen, F., Müller, K.-R., and Samek, W. (2015) · 2015
Earlier work this paper cites.
Evaluating the Visualization of What a Deep Neural Network Has Learned
Samek, W., Binder, A., Montavon, G., Lapuschkin, S., and Muller, K.-R. (2017) · 2017
Earlier work this paper cites.
Peeking inside the black-box: a survey on explainable artificial intelligence (xai)
Adadi, A. and Berrada, M. (2018) · 2018
Earlier work this paper cites.
Sanity checks for saliency maps
Adebayo, J., Gilmer, J., Muelly, M., Goodfellow, I., Hardt, M., and Kim, B. (2018) · 2018
Earlier work this paper cites.
Towards robust interpretability with self-explaining neural networks
Alvarez Melis, D. and Jaakkola, T. (2018) · 2018
Earlier work this paper cites.
Towards better understanding of gradient-based attribution methods for deep neural networks
Ancona, M., Ceolini, E., Öztireli, C., and Gross, M. (2018) · 2018
Earlier work this paper cites.
Explainable artificial intelligence: A survey
Došilović, F. K., Brčić, M., and Hlupić, N. (2018) · 2018
Earlier work this paper cites.
Methods for interpreting and understanding deep neural networks
Montavon, G., Samek, W., and Müller, K.-R. (2018) · 2018
Earlier work this paper cites.
Explainable agents and robots: Results from a systematic literature review
Anjomshoae, S., Najjar, A., Calvaresi, D., and Främling, K. (2019) · 2019
Cited alongside, same era.
Testing the robustness of attribution methods for convolutional neural networks in mri-based alzheimer’s disease classification
Eitel, F., Ritter, K., and (ADNI), A. D. N. I. (2019) · 2019
Cited alongside, same era.
Explanation in artificial intelligence: Insights from the social sciences
Miller, T. (2019) · 2019
Cited alongside, same era.
On the (in) fidelity and sensitivity of explanations
Yeh, C.-K., Hsieh, C.-Y., Suggala, A., Inouye, D. I., and Ravikumar, P. K. (2019) · 2019
Cited alongside, same era.
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
Barredo Arrieta, A., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., Garcia, S., Gil-Lopez, S., Molina, D., Benjamins, R., Chatila, R., and Herrera, F. (2020) · 2020
Cited alongside, same era.
The disagreement problem in explainable machine learning: A practitioner’s perspective
Krishna, S., Han, T., Gu, A., Pombra, J., Jabbari, S., Wu, S., and Lakkaraju, H. (2022) · 2022
Later among the works it cites.
Explainable artificial intelligence: a comprehensive review
Minh, D., Wang, H. X., Li, Y. F., and Nguyen, T. N. (2022) · 2022
Later among the works it cites.
Evaluating explainable artificial intelligence for x-ray image analysis
Miró-Nicolau, M., Moyà-Alcover, G., and Jaume-i Capó, A. (2022) · 2022
Later among the works it cites.
Generating perturbation-based explanations with robustness to out-of-distribution data
Qiu, L., Yang, Y., Cao, C. C., Zheng, Y., Ngai, H., Hsiao, J., and Chen, L. (2022) · 2022
Later among the works it cites.
Explainable artificial intelligence (XAI) in deep learning-based medical image analysis
van der Velden, B. H., Kuijf, H. J., Gilhuijs, K. G., and Viergever, M. A. (2022) · 2022
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Irof: a low resource evaluation metric for explanation methods
Rieger, L. and Hansen, L. K. (2020) · 2020
Cited alongside, same era.
Sanity checks for saliency metrics
Tomsett, R., Harborne, D., Chakraborty, S., Gurram, P., and Preece, A. (2020) · 2020
Cited alongside, same era.
Covid-net: A tailored deep convolutional neural network design for detection of covid-19 cases from chest x-ray images
Wang, L., Lin, Z. Q., and Wong, A. (2020) · 2020
Cited alongside, same era.
Evaluating and aggregating feature-based model explanations
Bhatt, U., Weller, A., and Moura, J. M. (2021) · 2021
Cited alongside, same era.
Evaluating local explanation methods on ground truth
Guidotti, R. (2021) · 2021
Cited alongside, same era.
A multidisciplinary survey and framework for design and evaluation of explainable ai systems
Mohseni, S., Zarei, N., and Ragan, E. D. (2021) · 2021
Cited alongside, same era.
Metrics for saliency map evaluation of deep learning explanation methods
Gomez, T., Fréour, T., and Mouchère, H. (2022) · 2022
Cited alongside, same era.
Fair and explainable depression detection in social media
Adarsh, V., Kumar, P. A., Lavanya, V., and Gangadharan, G. (2023) · 2023
Later among the works it cites.
A survey on xai and natural language explanations
Cambria, E., Malandri, L., Mercorio, F., Mezzanzanica, M., and Nobani, N. (2023) · 2023
Later among the works it cites.
The meta-evaluation problem in explainable ai: Identifying reliable estimators with metaquantus
Hedström, A., Bommer, P., Wickstrøm, K. K., Samek, W., Lapuschkin, S., and Höhne, M. M.-C. (2023) · 2023
Later among the works it cites.
Quantus: An explainable ai toolkit for responsible evaluation of neural network explanations and beyond
Hedström, A., Weber, L., Krakowczyk, D., Bareeva, D., Motzkus, F., Samek, W., Lapuschkin, S., and Höhne, M. M. M. (2023) · 2023
Later among the works it cites.
A novel approach to generate datasets with xai ground truth to evaluate image models
Miró-Nicolau, M., Jaume-i Capó, A., and Moyà-Alcover, G. (2023) · 2023
Later among the works it cites.
Miró-Nicolau, M., Jaume-i Capó, A., and Moyà-Alcover, G. (2023) · 2023
Later among the works it cites.
Chestx-ray8: Hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases
Wang, X., Peng, Y., Lu, L., Lu, Z., Bagheri, M., and Summers, R. M. (2017) · 2097
Closest in time.