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New Generation Computing

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01-02-2023

Explainable AI: To Reveal the Logic of Black-Box Models

Authors: Chinu, Urvashi Bansal

Published in: New Generation Computing

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Abstract

Artificial intelligence (AI) is continuously evolving; however, in the last 10 years, it has gotten considerably more difficult to explain AI models. With the help of explanations, end users can understand the outcomes generated by AI models. The proposed work has shown major issues and gaps in the literature. The main issues found in the literature are unfair/biased decisions made by the model, poor accuracy, reliability, and evaluation metrics to assess the effectiveness of explanations and security of data. Research results obtained in this proposed work highlight the needs, challenges, and opportunities in the field of Explainable artificial intelligence (XAI). How can we make artificial intelligence models explainable? Evaluation of explanations using metrics is the main contribution of this research work. Moreover, the proposed work analyzed different types of explanations, leading companies providing Explainable artificial intelligence services, and open-source tools available in the market for using Explainable artificial intelligence. Finally, based on the reviewed works, the proposed work well-found some future directions for designing more transparent models for artificial intelligence.

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Literature
1.
2.
Linardatos, P., Papastefanopoulos, V., Kotsiantis, S.: Explainable ai: A review of machine learning interpretability methods. Entropy 23(1), 18 (2020) CrossRef
3.
Mueller, S.T., Hoffman, R.R., Clancey, W., Emrey, A., Klein, G.: Explanation in human-ai systems: A literature meta-review, synopsis of key ideas and publications, and bibliography for explainable ai. arXiv preprint arXiv:​1902.​01876 (2019)
4.
Wong, L.J., McPherson, S.: Explainable neural network-based modulation classification via concept bottleneck models. In: 2021 IEEE 11th Annual Computing and Communication Workshop and Conference (CCWC), pp. 0191–0196 (2021). IEEE
5.
Kim, M.S., Yun, J.P., Park, P.: An explainable convolutional neural network for fault diagnosis in linear motion guide. IEEE Trans. Industr. Inf. 17(6), 4036–4045 (2020) CrossRef
6.
Karn, R.R., Kudva, P., Huang, H., Suneja, S., Elfadel, I.M.: Cryptomining detection in container clouds using system calls and explainable machine learning. IEEE Trans. Parallel Distrib. Syst. 32(3), 674–691 (2020) CrossRef
7.
Magesh, P.R., Myloth, R.D., Tom, R.J.: An explainable machine learning model for early detection of parkinson’s disease using lime on datscan imagery. Comput. Biol. Med. 126, 104041 (2020) CrossRef
8.
Antwarg, L., Miller, R.M., Shapira, B., Rokach, L.: Explaining anomalies detected by autoencoders using shapley additive explanations. Expert Syst. Appl. 186, 115736 (2021) CrossRef
9.
La Gatta, V., Moscato, V., Postiglione, M., Sperlì, G.: Pastle: Pivot-aided space transformation for local explanations. Pattern Recogn. Lett. 149, 67–74 (2021) CrossRef
10.
Kiefer, S.: Case: Explaining text classifications by fusion of local surrogate explanation models with contextual and semantic knowledge. Information Fusion 77, 184–195 (2022) CrossRef
11.
Giudici, P., Raffinetti, E.: Shapley-lorenz explainable artificial intelligence. Expert Syst. Appl. 167, 114104 (2021) CrossRef
12.
Barbado, A., Corcho, Ó., Benjamins, R.: Rule extraction in unsupervised anomaly detection for model explainability: Application to oneclass svm. Expert Syst. Appl. 189, 116100 (2022) CrossRef
13.
Haldar, S., John, P.G., Saha, D.: Reliable counterfactual explanations for autoencoder based anomalies. In: 8th ACM IKDD CODS and 26th COMAD, pp. 83–91 (2021)
14.
Neves, I., Folgado, D., Santos, S., Barandas, M., Campagner, A., Ronzio, L., Cabitza, F., Gamboa, H.: Interpretable heartbeat classification using local model-agnostic explanations on ecgs. Comput. Biol. Med. 133, 104393 (2021) CrossRef
15.
Sachan, S., Yang, J.-B., Xu, D.-L., Benavides, D.E., Li, Y.: An explainable ai decision-support-system to automate loan underwriting. Expert Syst. Appl. 144, 113100 (2020) CrossRef
16.
Liu, Q., Huang, Z., Yin, Y., Chen, E., Xiong, H., Su, Y., Hu, G.: Ekt: Exercise-aware knowledge tracing for student performance prediction. IEEE Trans. Knowl. Data Eng. 33(1), 100–115 (2019) CrossRef
17.
Cheng, X., Wang, J., Li, H., Zhang, Y., Wu, L., Liu, Y.: A method to evaluate task-specific importance of spatio-temporal units based on explainable artificial intelligence. Int. J. Geogr. Inf. Sci. 35(10), 2002–2025 (2021) CrossRef
18.
Keele, S., et al.: Guidelines for performing systematic literature reviews in software engineering. Technical report, Technical report, ver. 2.3 ebse technical report. ebse (2007)
19.
Achtibat, R., Dreyer, M., Eisenbraun, I., Bosse, S., Wiegand, T., Samek, W., Lapuschkin, S.: From" where" to" what": Towards human-understandable explanations through concept relevance propagation. arXiv preprint arXiv:​2206.​03208 (2022)
20.
21.
with Python, E.A.: Gianfagna, Leonida and Di Cecco, Antonio. Springer, Berlin/Heidelberg (2021)
22.
Du, M., Liu, N., Hu, X.: Techniques for interpretable machine learning. Commun. ACM 63(1), 68–77 (2019) CrossRef
23.
Fiandrino, C., Attanasio, G., Fiore, M., Widmer, J.: Toward native explainable and robust ai in 6g networks: Current state, challenges and road ahead. Comput. Commun. 193, 47–52 (2022) CrossRef
24.
Vilone, G., Longo, L.: Notions of explainability and evaluation approaches for explainable artificial intelligence. Information Fusion 76, 89–106 (2021) CrossRef
25.
Shin, D.: The effects of explainability and causability on perception, trust, and acceptance: Implications for explainable ai. Int. J. Hum Comput Stud. 146, 102551 (2021) CrossRef
26.
Sharma, D.K., Mishra, J., Singh, A., Govil, R., Srivastava, G., Lin, J.C.-W.: Explainable artificial intelligence for cybersecurity. Comput. Electr. Eng. 103, 108356 (2022) CrossRef
27.
Yerlikaya, F.A., Bahtiyar, Ş: Data poisoning attacks against machine learning algorithms. Expert Syst. Appl. 208, 118101 (2022) CrossRef
28.
Himeur, Y., Sohail, S.S., Bensaali, F., Amira, A., Alazab, M.: Latest trends of security and privacy in recommender systems: A comprehensive review and future perspectives. Computers & Security, 102746 (2022)
29.
30.
Siering, M.: Explainability and fairness of regtech for regulatory enforcement: Automated monitoring of consumer complaints. Decis. Support Syst. 158, 113782 (2022) CrossRef
31.
Jin, W., Li, X., Fatehi, M., Hamarneh, G.: Guidelines and evaluation for clinical explainable ai on medical image analysis. arXiv preprint arXiv:​2202.​10553 (2022)
32.
33.
Bartler, A., Hinderer, D., Yang, B.: Grad-lam: Visualization of deep neural networks for unsupervised learning. In: 2020 28th European Signal Processing Conference (EUSIPCO), pp. 1407–1411 (2021). IEEE
34.
Ribeiro, M.T., Singh, S., Guestrin, C.: " why should i trust you?" explaining the predictions of any classifier. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1135–1144 (2016)
35.
Roshan, K., Zafar, A.: Using kernel shap xai method to optimize the network anomaly detection model. In: 2022 9th International Conference on Computing for Sustainable Global Development (INDIACom), pp. 74–80 (2022). IEEE
36.
Karim, M.R., Jiao, J., Döhmen, T., Cochez, M., Beyan, O., Rebholz-Schuhmann, D., Decker, S.: Deepkneeexplainer: explainable knee osteoarthritis diagnosis from radiographs and magnetic resonance imaging. IEEE Access 9, 39757–39780 (2021) CrossRef
37.
Shrikumar, A., Greenside, P., Kundaje, A.: Learning important features through propagating activation differences. In: International Conference on Machine Learning, pp. 3145–3153 (2017). PMLR
38.
Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017)
39.
Apley, D.W., Zhu, J.: Visualizing the effects of predictor variables in black box supervised learning models. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 82(4), 1059–1086 (2020) MATHCrossRef
40.
Chen, J., Song, L., Wainwright, M., Jordan, M.: Learning to explain: An information-theoretic perspective on model interpretation. In: International Conference on Machine Learning, pp. 883–892 (2018). PMLR
41.
Sundararajan, M., Taly, A., Yan, Q.: Axiomatic attribution for deep networks. In: International Conference on Machine Learning, pp. 3319–3328 (2017). PMLR
42.
Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:​1409.​0473 (2014)
43.
Stepin, I., Alonso, J.M., Catala, A., Pereira-Fariña, M.: A survey of contrastive and counterfactual explanation generation methods for explainable artificial intelligence. IEEE Access 9, 11974–12001 (2021) CrossRef
44.
Mothilal, R.K., Sharma, A., Tan, C.: Explaining machine learning classifiers through diverse counterfactual explanations. In: Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency, pp. 607–617 (2020)
45.
Sharma, S., Henderson, J., Ghosh, J.: Certifai: Counterfactual explanations for robustness, transparency, interpretability, and fairness of artificial intelligence models. arXiv preprint arXiv:​1905.​07857 (2019)
46.
47.
Gurumoorthy, K.S., Dhurandhar, A., Cecchi, G., Aggarwal, C.: Efficient data representation by selecting prototypes with importance weights. In: 2019 IEEE International Conference on Data Mining (ICDM), pp. 260–269 (2019). IEEE
48.
Looveren, A.V., Klaise, J.: Interpretable counterfactual explanations guided by prototypes. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 650–665 (2021). Springer
49.
Kumar, D., Wong, A., Taylor, G.W.: Explaining the unexplained: A class-enhanced attentive response (clear) approach to understanding deep neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 36–44 (2017)
50.
Dhurandhar, A., Chen, P.-Y., Luss, R., Tu, C.-C., Ting, P., Shanmugam, K., Das, P.: Explanations based on the missing: Towards contrastive explanations with pertinent negatives. Advances in neural information processing systems 31 (2018)
51.
Poyiadzi, R., Sokol, K., Santos-Rodriguez, R., De Bie, T., Flach, P.: Face: feasible and actionable counterfactual explanations. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 344–350 (2020)
52.
Kim, B., Wattenberg, M., Gilmer, J., Cai, C., Wexler, J., Viegas, F., et al.: Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). In: International Conference on Machine Learning, pp. 2668–2677 (2018). PMLR
53.
Thiagarajan, J.J., Kailkhura, B., Sattigeri, P., Ramamurthy, K.N.: Treeview: Peeking into deep neural networks via feature-space partitioning. arXiv preprint arXiv:​1611.​07429 (2016)
54.
Rauber, P.E., Fadel, S.G., Falcao, A.X., Telea, A.C.: Visualizing the hidden activity of artificial neural networks. IEEE Trans. Visual Comput. Graphics 23(1), 101–110 (2016) CrossRef
55.
Sasaki, H., Hidaka, Y., Igarashi, H.: Explainable deep neural network for design of electric motors. IEEE Trans. Magn. 57(6), 1–4 (2021) CrossRef
56.
Li, L., Wang, B., Verma, M., Nakashima, Y., Kawasaki, R., Nagahara, H.: Scouter: Slot attention-based classifier for explainable image recognition. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 1046–1055 (2021)
57.
Zhou, Y., Zhu, Y., Ye, Q., Qiu, Q., Jiao, J.: Weakly supervised instance segmentation using class peak response. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3791–3800 (2018)
58.
Liu, G., Gifford, D.: Visualizing feature maps in deep neural networks using deepresolve. a genomics case study. In: Proceedings of the International Conference on Machine Learning-Workshop on Visualization for Deep Learning, Sydney, Australia, pp. 32–41 (2017)
59.
Lu, J., Yang, J., Batra, D., Parikh, D.: Hierarchical question-image co-attention for visual question answering. Advances in neural information processing systems 29 (2016)
60.
Das, A., Kottur, S., Gupta, K., Singh, A., Yadav, D., Moura, J.M., Parikh, D., Batra, D.: Visual dialog. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 326–335 (2017)
61.
62.
63.
Dong, Y., Su, H., Zhu, J., Zhang, B.: Improving interpretability of deep neural networks with semantic information. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4306–4314 (2017)
64.
Ribeiro, M.T., Singh, S., Guestrin, C.: Nothing else matters: Model-agnostic explanations by identifying prediction invariance. arXiv preprint arXiv:​1611.​05817 (2016)
65.
Vásquez-Morales, G.R., Martinez-Monterrubio, S.M., Moreno-Ger, P., Recio-Garcia, J.A.: Explainable prediction of chronic renal disease in the colombian population using neural networks and case-based reasoning. Ieee Access 7, 152900–152910 (2019) CrossRef
66.
Alvarez Melis, D., Jaakkola, T.: Towards robust interpretability with self-explaining neural networks. Advances in neural information processing systems 31 (2018)
67.
Seibold, C., Hilsmann, A., Eisert, P.: Focused lrp: Explainable ai for face morphing attack detection. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 88–96 (2021)
68.
Kapishnikov, A., Bolukbasi, T., Viégas, F., Terry, M.: Xrai: Better attributions through regions. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 4948–4957 (2019)
69.
Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., Torralba, A.: Learning deep features for discriminative localization. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2921–2929 (2016)
70.
Lamy, J.-B., Sekar, B., Guezennec, G., Bouaud, J., Séroussi, B.: Explainable artificial intelligence for breast cancer: A visual case-based reasoning approach. Artif. Intell. Med. 94, 42–53 (2019) CrossRef
71.
Samek, W., Montavon, G., Binder, A., Lapuschkin, S., Müller, K.-R.: Interpreting the predictions of complex ml models by layer-wise relevance propagation. arXiv preprint arXiv:​1611.​08191 (2016)
72.
Belle, V., Papantonis, I.: Principles and practice of explainable machine learning. Frontiers in big Data, 39 (2021)
73.
74.
Sharma, S., Henderson, J., Ghosh, J.: Certifai: A common framework to provide explanations and analyse the fairness and robustness of black-box models. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 166–172 (2020)
75.
Wu, H., Chen, W., Xu, S., Xu, B.: Counterfactual supporting facts extraction for explainable medical record based diagnosis with graph network. In: Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 1942–1955 (2021)
76.
Kim, B., Khanna, R., Koyejo, O.O.: Examples are not enough, learn to criticize! criticism for interpretability. Advances in neural information processing systems 29 (2016)
77.
78.
Wood-Doughty, Z., Cachola, I., Dredze, M.: Model distillation for faithful explanations of medical code predictions. In: Proceedings of the 21st Workshop on Biomedical Language Processing, pp. 412–425 (2022)
79.
Alharbi, R., Vu, M.N., Thai, M.T.: Learning interpretation with explainable knowledge distillation. In: 2021 IEEE International Conference on Big Data (Big Data), pp. 705–714 (2021). IEEE
80.
Dalleiger, S., Vreeken, J.: Explainable data decompositions. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 3709–3716 (2020)
81.
82.
Greenwell, B.M., Boehmke, B.C., McCarthy, A.J.: A simple and effective model-based variable importance measure. arXiv preprint arXiv:​1805.​04755 (2018)
83.
84.
Samek, W., Müller, K.-R.: Towards explainable artificial intelligence. In: Explainable AI: Interpreting, Explaining and Visualizing Deep Learning, pp. 5–22. Springer, ??? (2019)
85.
Chittajallu, D.R., Dong, B., Tunison, P., Collins, R., Wells, K., Fleshman, J., Sankaranarayanan, G., Schwaitzberg, S., Cavuoto, L., Enquobahrie, A.: Xai-cbir: Explainable ai system for content based retrieval of video frames from minimally invasive surgery videos. In: 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), pp. 66–69 (2019). IEEE
86.
Aas, K., Jullum, M., Løland, A.: Explaining individual predictions when features are dependent: More accurate approximations to shapley values. Artif. Intell. 298, 103502 (2021) MATHCrossRef
87.
Almutairi, M., Stahl, F., Bramer, M.: Reg-rules: an explainable rule-based ensemble learner for classification. IEEE Access 9, 52015–52035 (2021) CrossRef
88.
Keneni, B.M., Kaur, D., Al Bataineh, A., Devabhaktuni, V.K., Javaid, A.Y., Zaientz, J.D., Marinier, R.P.: Evolving rule-based explainable artificial intelligence for unmanned aerial vehicles. IEEE Access 7, 17001–17016 (2019) CrossRef
89.
Mahbooba, B., Timilsina, M., Sahal, R., Serrano, M.: Explainable artificial intelligence (xai) to enhance trust management in intrusion detection systems using decision tree model. Complexity 2021 (2021)
90.
Loyola-Gonzalez, O., Gutierrez-Rodríguez, A.E., Medina-Pérez, M.A., Monroy, R., Martínez-Trinidad, J.F., Carrasco-Ochoa, J.A., Garcia-Borroto, M.: An explainable artificial intelligence model for clustering numerical databases. IEEE Access 8, 52370–52384 (2020) CrossRef
91.
Wachter, S., Mittelstadt, B., Russell, C.: Counterfactual explanations without opening the black box: Automated decisions and the gdpr. Harv. JL & Tech. 31, 841 (2017)
92.
Kouki, P., Schaffer, J., Pujara, J., O’Donovan, J., Getoor, L.: Generating and understanding personalized explanations in hybrid recommender systems. ACM Transactions on Interactive Intelligent Systems (TiiS) 10(4), 1–40 (2020) CrossRef
93.
94.
Guidotti, R.: Evaluating local explanation methods on ground truth. Artif. Intell. 291, 103428 (2021) MATHCrossRef
95.
Sokol, K., Flach, P.: Explainability fact sheets: a framework for systematic assessment of explainable approaches. In: Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency, pp. 56–67 (2020)
96.
97.
Dieber, J., Kirrane, S.: A novel model usability evaluation framework (muse) for explainable artificial intelligence. Information Fusion 81, 143–153 (2022) CrossRef
98.
Lin, Y.-S., Lee, W.-C., Celik, Z.B.: What do you see? evaluation of explainable artificial intelligence (xai) interpretability through neural backdoors. arXiv preprint arXiv:​2009.​10639 (2020)
99.
Ozyegen, O., Ilic, I., Cevik, M.: Evaluation of interpretability methods for multivariate time series forecasting. Appl. Intell. 52(5), 4727–4743 (2022) CrossRef
100.
Löfström, H., Hammar, K., Johansson, U.: A meta survey of quality evaluation criteria in explanation methods. In: International Conference on Advanced Information Systems Engineering, pp. 55–63 (2022). Springer
101.
Nauta, M., Trienes, J., Pathak, S., Nguyen, E., Peters, M., Schmitt, Y., Schlötterer, J., van Keulen, M., Seifert, C.: From anecdotal evidence to quantitative evaluation methods: A systematic review on evaluating explainable ai. arXiv preprint arXiv:​2201.​08164 (2022)
102.
Bibal, A., Frénay, B.: Interpretability of machine learning models and representations: an introduction. In: ESANN (2016)
103.
104.
Anysz, H., Brzozowski, Ł, Kretowicz, W., Narloch, P.: Feature importance of stabilised rammed earth components affecting the compressive strength calculated with explainable artificial intelligence tools. Materials 13(10), 2317 (2020) CrossRef
105.
Zhou, J., Gandomi, A.H., Chen, F., Holzinger, A.: Evaluating the quality of machine learning explanations: A survey on methods and metrics. Electronics 10(5), 593 (2021) CrossRef
106.
Rosenfeld, A.: Better metrics for evaluating explainable artificial intelligence. In: Proceedings of the 20th International Conference on Autonomous Agents and Multiagent Systems, pp. 45–50 (2021)
107.
Kavya, R., Christopher, J., Panda, S., Lazarus, Y.B.: Machine learning and xai approaches for allergy diagnosis. Biomed. Signal Process. Control 69, 102681 (2021) CrossRef
108.
Amoroso, N., Pomarico, D., Fanizzi, A., Didonna, V., Giotta, F., La Forgia, D., Latorre, A., Monaco, A., Pantaleo, E., Petruzzellis, N., et al.: A roadmap towards breast cancer therapies supported by explainable artificial intelligence. Appl. Sci. 11(11), 4881 (2021) CrossRef
109.
Chan, M.-C., Pai, K.-C., Su, S.-A., Wang, M.-S., Wu, C.-L., Chao, W.-C.: Explainable machine learning to predict long-term mortality in critically ill ventilated patients: a retrospective study in central taiwan. BMC Med. Inform. Decis. Mak. 22(1), 1–11 (2022) CrossRef
110.
Peng, J., Zou, K., Zhou, M., Teng, Y., Zhu, X., Zhang, F., Xu, J.: An explainable artificial intelligence framework for the deterioration risk prediction of hepatitis patients. J. Med. Syst. 45(5), 1–9 (2021) CrossRef
111.
Chen, J., Dai, X., Yuan, Q., Lu, C., Huang, H.: Towards interpretable clinical diagnosis with bayesian network ensembles stacked on entity-aware cnns. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3143–3153 (2020)
112.
Rucco, M., Viticchi, G., Falsetti, L.: Towards personalized diagnosis of glioblastoma in fluid-attenuated inversion recovery (flair) by topological interpretable machine learning. Mathematics 8(5), 770 (2020) CrossRef
113.
Gu, D., Li, Y., Jiang, F., Wen, Z., Liu, S., Shi, W., Lu, G., Zhou, C.: Vinet: A visually interpretable image diagnosis network. IEEE Trans. Multimedia 22(7), 1720–1729 (2020) CrossRef
114.
Laios, A., Kalampokis, E., Johnson, R., Thangavelu, A., Tarabanis, C., Nugent, D., De Jong, D.: Explainable artificial intelligence for prediction of complete surgical cytoreduction in advanced-stage epithelial ovarian cancer. Journal of personalized medicine 12(4), 607 (2022) CrossRef
115.
Wesołowski, S., Lemmon, G., Hernandez, E.J., Henrie, A., Miller, T.A., Weyhrauch, D., Puchalski, M.D., Bray, B.E., Shah, R.U., Deshmukh, V.G., et al.: An explainable artificial intelligence approach for predicting cardiovascular outcomes using electronic health records. PLOS digital health 1(1), 0000004 (2022) CrossRef
116.
Lucieri, A., Bajwa, M.N., Braun, S.A., Malik, M.I., Dengel, A., Ahmed, S.: Exaid: A multimodal explanation framework for computer-aided diagnosis of skin lesions. Comput. Methods Programs Biomed. 215, 106620 (2022) CrossRef
117.
Holzinger, A., Biemann, C., Pattichis, C.S., Kell, D.B.: What do we need to build explainable ai systems for the medical domain? arXiv preprint arXiv:​1712.​09923 (2017)
118.
van der Waa, J., Nieuwburg, E., Cremers, A., Neerincx, M.: Evaluating xai: A comparison of rule-based and example-based explanations. Artif. Intell. 291, 103404 (2021) MATHCrossRef
119.
Alonso, J.M.: Teaching explainable artificial intelligence to high school students. International Journal of Computational Intelligence Systems 13(1), 974–987 (2020) CrossRef
120.
Mirchi, N., Bissonnette, V., Yilmaz, R., Ledwos, N., Winkler-Schwartz, A., Del Maestro, R.F.: The virtual operative assistant: An explainable artificial intelligence tool for simulation-based training in surgery and medicine. PLoS ONE 15(2), 0229596 (2020) CrossRef
121.
Kim, J., Canny, J.: Interpretable learning for self-driving cars by visualizing causal attention. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2942–2950 (2017)
122.
Chen, H.-Y., Lee, C.-H.: Vibration signals analysis by explainable artificial intelligence (xai) approach: Application on bearing faults diagnosis. IEEE Access 8, 134246–134256 (2020) CrossRef
123.
Serradilla, O., Zugasti, E., Cernuda, C., Aranburu, A., de Okariz, J.R., Zurutuza, U.: Interpreting remaining useful life estimations combining explainable artificial intelligence and domain knowledge in industrial machinery. In: 2020 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), pp. 1–8 (2020). IEEE
124.
Sarp, S., Kuzlu, M., Cali, U., Elma, O., Guler, O.: An interpretable solar photovoltaic power generation forecasting approach using an explainable artificial intelligence tool. In: 2021 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT), pp. 1–5 (2021). IEEE
125.
Carletti, M., Masiero, C., Beghi, A., Susto, G.A.: Explainable machine learning in industry 4.0: Evaluating feature importance in anomaly detection to enable root cause analysis. In: 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC), pp. 21–26 (2019). IEEE
126.
Rehse, J.-R., Mehdiyev, N., Fettke, P.: Towards explainable process predictions for industry 4.0 in the dfki-smart-lego-factory. KI-Künstliche Intelligenz 33(2), 181–187 (2019)
127.
Ferreyra, E., Hagras, H., Kern, M., Owusu, G.: Depicting decision-making: A type-2 fuzzy logic based explainable artificial intelligence system for goal-driven simulation in the workforce allocation domain. In: 2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), pp. 1–6 (2019). IEEE
128.
Shalaeva, V., Alkhoury, S., Marinescu, J., Amblard, C., Bisson, G.: Multi-operator decision trees for explainable time-series classification. In: International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, pp. 86–99 (2018). Springer
129.
Suh, J., Yoo, S., Park, J., Cho, S.Y., Cho, M.C., Son, H., Jeong, H.: Development and validation of an explainable artificial intelligence-based decision-supporting tool for prostate biopsy. BJU Int. 126(6), 694–703 (2020) CrossRef
130.
Karlsson, I., Rebane, J., Papapetrou, P., Gionis, A.: Locally and globally explainable time series tweaking. Knowl. Inf. Syst. 62(5), 1671–1700 (2020) CrossRef
131.
Jung, A., Nardelli, P.H.: An information-theoretic approach to personalized explainable machine learning. IEEE Signal Process. Lett. 27, 825–829 (2020) CrossRef
132.
Gedikli, F., Jannach, D., Ge, M.: How should i explain? a comparison of different explanation types for recommender systems. Int. J. Hum Comput Stud. 72(4), 367–382 (2014) CrossRef
133.
134.
135.
Carta, S., Podda, A.S., Reforgiato Recupero, D., Stanciu, M.M.: Explainable ai for financial forecasting. In: International Conference on Machine Learning, Optimization, and Data Science, pp. 51–69 (2021). Springer
136.
Kuiper, O., Berg, M.v.d., Burgt, J.v.d., Leijnen, S.: Exploring explainable ai in the financial sector: perspectives of banks and supervisory authorities. In: Benelux Conference on Artificial Intelligence, pp. 105–119 (2021). Springer
137.
He, X., Chen, T., Kan, M.-Y., Chen, X.: Trirank: Review-aware explainable recommendation by modeling aspects. In: Proceedings of the 24th ACM International on Conference on Information and Knowledge Management, pp. 1661–1670 (2015)
138.
Loyola-González, O.: Understanding the criminal behavior in mexico city through an explainable artificial intelligence model. In: Mexican International Conference on Artificial Intelligence, pp. 136–149 (2019). Springer
139.
Sarathy, N., Alsawwaf, M., Chaczko, Z.: Investigation of an innovative approach for identifying human face-profile using explainable artificial intelligence. In: 2020 IEEE 18th International Symposium on Intelligent Systems and Informatics (SISY), pp. 155–160 (2020). IEEE
140.
Callegari, C., Ducange, P., Fazzolari, M., Vecchio, M.: Explainable internet traffic classification. Appl. Sci. 11(10), 4697 (2021) CrossRef
141.
Wickramasinghe, C.S., Amarasinghe, K., Marino, D.L., Rieger, C., Manic, M.: Explainable unsupervised machine learning for cyber-physical systems. IEEE Access 9, 131824–131843 (2021) CrossRef
142.
Solanke, A.A.: Explainable digital forensics ai: Towards mitigating distrust in ai-based digital forensics analysis using interpretable models. Forensic Science International: Digital Investigation 42, 301403 (2022)
143.
Díaz-Rodríguez, N., Pisoni, G.: Accessible cultural heritage through explainable artificial intelligence. In: Adjunct Publication of the 28th ACM Conference on User Modeling, Adaptation and Personalization, pp. 317–324 (2020)
144.
Segura, V., Brandão, B., Fucs, A., Vital Brazil, E.: Towards explainable ai using similarity: An analogues visualization system. In: International Conference on Human-Computer Interaction, pp. 389–399 (2019). Springer
145.
Zhong, Q., Fan, X., Luo, X., Toni, F.: An explainable multi-attribute decision model based on argumentation. Expert Syst. Appl. 117, 42–61 (2019) CrossRef
146.
Baptista, M.L., Goebel, K., Henriques, E.M.: Relation between prognostics predictor evaluation metrics and local interpretability shap values. Artif. Intell. 306, 103667 (2022) MATHCrossRef
147.
Futia, G., Vetrò, A.: On the integration of knowledge graphs into deep learning models for a more comprehensible ai-three challenges for future research. Information 11(2), 122 (2020) CrossRef
148.
Tiddi, I., Schlobach, S.: Knowledge graphs as tools for explainable machine learning: A survey. Artif. Intell. 302, 103627 (2022) MATHCrossRef
149.
Rajabi, E., Kafaie, S.: Knowledge graphs and explainable ai in healthcare. Information 13(10), 459 (2022) CrossRef
150.
Rožanec, J.M., Zajec, P., Kenda, K., Novalija, I., Fortuna, B., Mladenić, D.: Xai-kg: knowledge graph to support xai and decision-making in manufacturing. In: International Conference on Advanced Information Systems Engineering, pp. 167–172 (2021). Springer
151.
Díaz-Rodríguez, N., Lamas, A., Sanchez, J., Franchi, G., Donadello, I., Tabik, S., Filliat, D., Cruz, P., Montes, R., Herrera, F.: Explainable neural-symbolic learning (x-nesyl) methodology to fuse deep learning representations with expert knowledge graphs: The monumai cultural heritage use case. Information Fusion 79, 58–83 (2022) CrossRef
152.
Bennetot, A., Franchi, G., Del Ser, J., Chatila, R., Diaz-Rodriguez, N.: Greybox xai: a neural-symbolic learning framework to produce interpretable predictions for image classification. Knowl.-Based Syst. 258, 109947 (2022) CrossRef
153.
Chen, H., Deng, S., Zhang, W., Xu, Z., Li, J., Kharlamov, E.: Neural symbolic reasoning with knowledge graphs: Knowledge extraction, relational reasoning, and inconsistency checking. Fundamental Research 1(5), 565–573 (2021) CrossRef
154.
Sharma, D.K., Mishra, J., Singh, A., Govil, R., Srivastava, G., Lin, J.C.-W.: Explainable artificial intelligence for cybersecurity. Comput. Electr. Eng. 103, 108356 (2022) CrossRef
Metadata
Title
Explainable AI: To Reveal the Logic of Black-Box Models
Authors
Chinu
Urvashi Bansal
Publication date
01-02-2023
Publisher
Ohmsha
Published in
New Generation Computing
Print ISSN: 0288-3635
Electronic ISSN: 1882-7055
DOI
https://doi.org/10.1007/s00354-022-00201-2

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