CoChat
Sign Up
Cited by 403Explainable Artificial Intelligence (XAI)Software Testing and Debugging TechniquesAdvanced Neural Network Applications

Analysis of Explainers of Black Box Deep Neural Networks for Computer Vision: A Survey

Open access
Buhrmester, Vanessa, Münch, David, Arens, Michael·2021·Machine Learning and Knowledge Extraction
DOI 10.3390/make3040048
Open this paper in CoChat — free accountOpen PDF

Abstract

Deep Learning is a state-of-the-art technique to make inference on extensive or complex data. As a black box model due to their multilayer nonlinear structure, Deep Neural Networks are often criticized as being non-transparent and their predictions not traceable by humans. Furthermore, the models learn from artificially generated datasets, which often do not reflect reality. By basing decision-making algorithms on Deep Neural Networks, prejudice and unfairness may be promoted unknowingly due to a lack of transparency. Hence, several so-called explanators, or explainers, have been developed. Explainers try to give insight into the inner structure of machine learning black boxes by analyzing the connection between the input and output. In this survey, we present the mechanisms and properties of explaining systems for Deep Neural Networks for Computer Vision tasks. We give a comprehensive overview about the taxonomy of related studies and compare several survey papers that deal with explainability in general. We work out the drawbacks and gaps and summarize further research ideas.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.3390/make3040048

Cites

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Organizations will lose trust in "black box" AI systems until they implement explainability mechanisms and auditability controls.

    “The results from this study support prior studies demonstrating that organizations will lose trust in “black box” AI systems until they implement explainability mechanisms and auditability controls (Buhrmester et al. 2021; Hassija et al. 2024).”

    From AI-driven financial fraud detection in Pakistan’s banking sector: bridging strategic intent and operational implementation (2026) · cites this paper as Buhrmester et al. 2021

    Section 2.4 (page not provided) reports that trusting an explanation is important to trusting a black-box prediction model. The Introduction also describes black-box systems as difficult for users to understand. Neither passage establishes that organizations will lose trust until they implement explainability mechanisms and auditability controls; the paper does not present that organizational or conditional claim. (LLM, conf=0.86)

    Experimental — accuracy not yet measured

  • Present neural networks are effectively black boxes, so we cannot formally verify that they will reliably behave as intended.

    “First, since present neural networks are effectively black boxes [Buhrmester et al., 2021], we cannot formally verify that they will reliably behave as intended, and need to rely more on informal analysis.”

    From The alignment problem from a deep learning perspective (2022) · cites this paper as Buhrmester et al., 2021

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

No citation lists for this paper

No citation lists for this paper yet — checked again daily.

References 126 · Cited by 403

Research works printed but not linked: 86

  1. Das, A.; Rad, P. Opportunities and challenges in explainable artificial intelligence (xai): A survey. arXiv 2020, arXiv:2006.11371. · arXiv
  2. Chen, Y.; Ouyang, L.; Bao, F.S.; Li, Q.; Han, L.; Zhu, B.; Ge, Y.; Robinson, P.; Xu, M.; Liu, J.; et al. An interpretable machine learning framework for accurate severe vs. non-severe COVID-19 clinical type classification. Lancet 2020. [CrossRef] · DOI
  3. Fan, X.; Liu, S.; Chen, J.; Henderson, T.C. An investigation of COVID-19 spreading factors with explainable ai techniques. arXiv 2020, arXiv:2005.06612. · arXiv
  4. Karim, M.; Döhmen, T.; Rebholz-Schuhmann, D.; Decker, S.; Cochez, M.; Beyan, O. Deepcovidexplainer: Explainable COVID-19 predictions based on chest X-ray images. arXiv 2020, arXiv:2004.04582. · arXiv
  5. Szegedy, C.; Zaremba, W.; Sutskever, I.; Bruna, J.; Erhan, D.; Goodfellow, I.; Fergus, R. Intriguing properties of neural networks. arXiv 2013, arXiv:1312.6199. · arXiv
  6. Goodfellow, I.J.; Shlens, J.; Szegedy, C. Explaining and harnessing adversarial examples. arXiv 2014, arXiv:1412.6572. · arXiv
  7. 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, San Francisco, CA, USA, 13-17 August 2016.
  8. Moosavi-Dezfooli, S.M. DeepFool: A simple and accurate method to fool deep neural networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA, 27-30 June 2016.
  9. Bose, A.J.; Aarabi, P. Adversarial attacks on face detectors using neural net based constrained optimization. In Proceedings of the IEEE 20th International Workshop on Multimedia Signal Processing (MMSP), Vancouver, BC, Canada, 29-31 August 2018.
  10. Jia, R.; Liang, P. Adversarial examples for evaluating reading comprehension systems. arXiv 2017, arXiv:1707.07328. · arXiv
  11. Eykholt, K.; Evtimov, I.; Fernandes, E.; Li, B.; Rahmati, A.; Xiao, C.; Prakash, A.; Kohno, T.; Song, D. Robust physical-world attacks on deep learning visual classification. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA, 18-23 June 2018.
  12. Ranjan, A.; Janai, J.; Geiger, A.; Black, M.J. Attacking Optical Flow. In Proceedings of the International Conference on Computer Vision, Seoul, Korea, 27-28 October 2019.
  13. Sharif, M.; Bhagavatula, S.; Bauer, L.; Reiter, M.K. Accessorize to a crime: Real and stealthy attacks on state-of-the-art face recognition. In Proceedings of the ACM SIGSAC Conference on Computer and Communications Security, Vienna, Austria, 24-28 October 2016.
  14. Sharif, M.; Bhagavatula, S.; Bauer, L.; Reiter, M.K. A General Framework for Adversarial Examples with Objectives. ACM Trans. Priv. Secur. (TOPS) 2019, 22, 1-30. [CrossRef] · DOI · Paper page
  15. Holmes, J.; Meyerhoff, M. The Handbook of Language and Gender; John Wiley & Sons: Hoboken, NJ, USA, 2008.
  16. Hirschberg, J.; Manning, C.D. Advances in natural language processing. Science 2015, 349, 261-266. [CrossRef] · DOI · Paper page
  17. Greenwald, A.G.; McGhee, D.E.; Schwartz, J.L. Measuring individual differences in implicit cognition: The implicit association test. J. Personal. Soc. Psychol. 1998, 74, 1464-1480. [CrossRef] · DOI · Paper page
  18. Font, J.E.; Costa-Jussa, M.R. Equalizing gender biases in neural machine translation with word embeddings techniques. arXiv 2019, arXiv:1901.03116. · arXiv
  19. Nguyen, A.; Yosinski, J.; Clune, J. Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Images. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA, 7-12 June 2015.
  20. Geirhos, R.; Rubisch, P.; Michaelis, C.; Bethge, M.; Wichmann, F.A.; Brendel, W. ImageNet-trained CNNs are biased towards texture; Increasing shape bias improves accuracy and robustness. arXiv 2018, arXiv:1811.12231. · arXiv
  21. Hastie, T.; Tibshirani, R. Generalized additive model (GAM). Stat. Sci. 1986, 1, 297-318.
  22. Friedman, J.; Hastie, T.; Tibshirani, R. The Elements of Statistical Learning; Springer Series in Statistics New York; Springer: New York, NY, USA, 2001.
  23. Salzberg, S.L. C4. 5: Programs for Machine Learning by J. Ross Quinlan. Morgan Kaufmann Publishers, Inc., 1993; Kluwer Academic: Boston, MA, USA, 1994.
  24. Goldstein, A.; Kapelner, A.; Bleich, J.; Pitkin, E. Peeking inside the black box: Visualizing statistical learning with plots of individual conditional expectation. J. Comput. Graph. Stat. 2015, 24, 44-65. [CrossRef] · DOI · Paper page
  25. Van Lent, M.; Fisher, W.; Mancuso, M. An explainable artificial intelligence system for small-unit tactical behavior. In Proceedings of the National Conference on Artificial Intelligence, San Jose, CA, USA, 25-29 July 2004.
  26. Shortliffe, E.H. Mycin: A knowledge-based computer program applied to infectious diseases. In Proceedings of the Annual Symposium on Computer Application in Medical Care, San Francisco, CA, USA, 5 October 1997; American Medical Informatics Association: Bethesda, MD, USA, 1977.
  27. Baehrens, D.; Schroeter, T.; Harmeling, S.; Kawanabe, M.; Hansen, K.; MÞller, K.R. How to explain individual classification decisions. J. Mach. Learn. Res. 2010, 11, 1803-1831.
  28. Kononenko, I. Inductive and Bayesian learning in medical diagnosis. Appl. Artif. Intell. Int. J. 1993, 7, 317-337. [CrossRef] · DOI
  29. Becker, B.; Kohavi, R.; Sommerfield, D. Visualizing the simple Bayesian classifier. In Information Visualization in Data Mining and Knowledge Discovery; Morgan Kaufmann Publishers Inc.: San Francisco, CA, USA, 2001; pp. 237-249. Available online: https://dl.acm.org/doi/10.5555/383784.383809 (accessed on 7 October 2021). · DOI
  30. Možina, M.; Demšar, J.; Kattan, M.; Zupan, B. Nomograms for visualization of naive Bayesian classifier. In European Conference on Principles of Data Mining and Knowledge Discovery, Proceedings of the 8th European Conference on Principles and Practice of Knowledge Discovery in Databases, Pisa, Italy, 20-24 September 2004; Springer: Berlin/Heidelberg, Germany, 2004.
  31. Poulet, F. Svm and graphical algorithms: A cooperative approach. In Proceedings of the Fourth IEEE International Conference on Data Mining (ICDM'04), Brighton, UK, 1-4 November 2004.
  32. Hamel, L. Visualization of support vector machines with unsupervised learning. In Proceedings of the IEEE Symposium on Computational Intelligence and Bioinformatics and Computational Biology, Toronto, ON, Canada, 28-29 September 2006.
  33. Breiman, L. Bagging predictors. Mach. Learn. 1996, 24, 123-140. [CrossRef] · DOI · Paper page
  34. Ciresan, D.; Giusti, A.; Gambardella, L.M.; Schmidhuber, J. Deep neural networks segment neuronal membranes in electron microscopy images. Adv. Neural Inf. Process. Syst. 2012, 25, 2843-2851.
  35. Ciresan, D.; Meier, U.; Schmidhuber, J. Multi-column deep neural networks for image classification. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Providence, RI, USA, 16-21 June 2012.
  36. Le, Q.V.; Zou, W.Y.; Yeung, S.Y.; Ng, A.Y. Learning hierarchical invariant spatio-temporal features for action recognition with independent subspace analysis. In Proceedings of the CVPR 2011, Colorado Springs, CO, USA, 20-25 June 2011.
  37. Rumelhart, D.E.; Hinton, G.E.; Williams, R.J. Learning representations by back-propagating errors. Nature 1986, 323, 533-536. [CrossRef] · DOI · Paper page
  38. Collobert, R.; Weston, J.; Bottou, L.; Karlen, M.; Kavukcuoglu, K.; Kuksa, P. Natural language processing (almost) from scratch. J. Mach. Learn. Res. 2011, 12, 2493-2537.
  39. Hochreiter, S.; Schmidhuber, J. Long short-term memory. Neural Comput. 1997, 9, 1735-1780. [CrossRef] · DOI · Paper page
  40. Guidotti, R.; Monreale, A.; Ruggieri, S.; Turini, F.; Giannotti, F.; Pedreschi, D. A survey of methods for explaining black box models. ACM Comput. Surv. (CSUR) 2019, 51, 1-42. [CrossRef] · DOI · Paper page
  41. Zeiler, M.D.; Fergus, R. Visualizing and Understanding Convolutional Networks; Springer: Berlin/Heidelberg, Germany, 2014.
  42. Mahendran, A.; Vedaldi, A. Understanding deep image representations by inverting them. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA, 7-12 June 2015.
  43. Bach, S.; Binder, A.; Montavon, G.; Klauschen, F.; Müller, K.R.; Samek, W. On pixel-wise explanations for non-linear classifier decisions by layerwise relevance propagation. PLoS ONE 2015, 10, e0130140. [CrossRef] · DOI · Paper page
  44. Springenberg, J.; Dosovitskiy, A.; Brox, T.; Riedmiller, M. Striving for Simplicity: The All Convolutional Net. In Proceedings of the ICLR (Workshop Track), San Diego, CA, USA, 7-9 May 2015.
  45. Zhang, R.; Isola, P.; Efros, A.A. Colorful image colorization. In European Conference on Computer Vision (ECCV), Proceedings of the 14th European Conference, Amsterdam, The Netherlands, 11-14 October 2016; Springer: Cham, Switzerland, 2016.
  46. Zhang, J.; Bargal, S.A.; Lin, Z.; Brandt, J.; Shen, X.; Sclaroff, S. Top-down neural attention by excitation backprop. Int. J. Comput. Vis. 2018, 126, 1084-1102. [CrossRef] · DOI
  47. Nguyen, A.; Dosovitskiy, A.; Yosinski, J.; Brox, T.; Clune, J. Synthesizing the preferred inputs for neurons in neural networks via deep generator networks. Adv. Neural Inf. Process. Syst. 2016, 29, 3387-3395.
  48. Yosinski, J.; Clune, J.; Nguyen, A.; Fuchs, T.; Lipson, H. Understanding neural networks through deep visualization. arXiv 2015, arXiv:1506.06579. · arXiv
  49. Dosovitskiy, A.; Brox, T. Inverting visual representations with convolutional networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA, 27-30 June 2016.
  50. Carter, S.; Armstrong, Z.; Schubert, L.; Johnson, I.; Olah, C. Activation Atlas. Distill 2019, 4, e15. [CrossRef] · DOI · Paper page
  51. Gilpin, L.H.; Bau, D.; Yuan, B.Z.; Bajwa, A.; Specter, M.; Kagal, L. Explaining explanations: An overview of interpretability of machine learning. In Proceedings of the IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA), Turin, Italy, 1-3 October 2018.
  52. Remler, D.K.; Van Ryzin, G.G. Research Methods in Practice: Strategies for Description and Causation; Sage Publications: Thousand Oaks, CA, USA, 2014.
  53. Ridgeway, G.; Madigan, D.; Richardson, T.; O'Kane, J. Interpretable Boosted Naïve Bayes Classification; AAAI: Palo Alto, CA, USA, 1998.
  54. Holzinger, A.; Malle, B.; Saranti, A.; Pfeifer, B. Towards multi-modal causability with Graph Neural Networks enabling information fusion for explainable AI. Inf. Fusion 2021, 71, 28-37. [CrossRef] · DOI · Paper page
  55. Martens, D.; Vanthienen, J.; Verbeke, W.; Baesens, B. Performance of classification models from a user perspective. Decis. Support Syst. 2011, 51, 782-793. [CrossRef] · DOI
  56. Freitas, A.A. Comprehensible classification models: A position paper. ACM SIGKDD Explor. Newsl. 2014, 15, 1-10. [CrossRef] · DOI
  57. Ustun, B.; Rudin, C. Methods and models for interpretable linear classification. arXiv 2014, arXiv:1405.4047. · arXiv
  58. Letham, B.; Rudin, C.; McCormick, T.H.; Madigan, D. An interpretable stroke prediction model using rules and Bayesian analysis. In Proceedings of the Workshops at the Twenty-Seventh AAAI Conference on Artificial Intelligence, Bellevue, WA, USA, 14-18 July 2013.
  59. Zilke, J.R.; Mencía, E.L.; Janssen, F. DeepRED-Rule extraction from deep neural networks. In International Conference on Discovery Science, Proceedings of the 19th International Conference, DS 2016, Bari, Italy, 19-21 October 2016; Springer: Cham, Switzerland, 2016.
  60. Fu, L. Rule generation from neural networks. IEEE Trans. Syst. Man Cybern. 1994, 24, 1114-1124.
  61. Craven, M.; Shavlik, J.W. Extracting tree-structured representations of trained networks. Adv. Neural Inf. Process. Syst. 1996, 8, 24-30.
  62. Hainmueller, J.; Hazlett, C. Kernel regularized least squares: Reducing misspecification bias with a flexible and interpretable machine learning approach. Politi. Anal. 2014, 22, 143-168. [CrossRef] · DOI
  63. Nie, W.; Zhang, Y.; Patel, A. A theoretical explanation for perplexing behaviors of backpropagation-based visualizations. In Proceedings of the International Conference on Machine Learning, Stockholm, Sweden, 10-15 July 2018; pp. 3809-3818.
  64. Kindermans, P.J.; Schütt, K.T.; Alber, M.; Müller, K.R.; Erhan, D.; Kim, B.; Dähne, S. Learning how to explain neural networks: Patternnet and patternattribution. arXiv 2017, arXiv:1705.05598. · arXiv
  65. Haufe, S.; Meinecke, F.; Görgen, K.; Dähne, S.; Haynes, J.D.; Blankertz, B.; Bießmann, F. On the interpretation of weight vectors of linear models in multivariate neuroimaging. Neuroimage 2014, 87, 96-110. [CrossRef] · DOI
  66. Lakkaraju, H.; Bach, S.H.; Leskovec, J. Interpretable decision sets: A joint framework for description and prediction. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA, 13-17 August 2016.
  67. Choi, E.; Bahadori, M.T.; Sun, J.; Kulas, J.; Schuetz, A.; Stewart, W. Retain: An interpretable predictive model for healthcare using reverse time attention mechanism. In Proceedings of the 30th International Conference on Neural Information Processing Systems, Barcelona, Spain, 5-10 December 2016.
  68. Shrikumar, A.; Greenside, P.; Kundaje, A. Learning important features through propagating activation differences. In Proceedings of the 34th International Conference on Machine Learning, Sydney, Australia, 6-11 August 2017; Volume 70.
  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, Las Vegas, NV, USA, 27-30 June 2016.
  70. 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, Venice, Italy, 22-29 October 2017.
  71. Chattopadhyay, A.; Sarkar, A.; Howlader, P.; Balasubramanian, V. Grad-CAM++: Generalized Gradient-based Visual Explanations for Deep Convolutional Networks. In Proceedings of the IEEE Winter Conference on Applications of Computer Vision (WACV), Lake Tahoe, NV, USA, 12-15 March 2018.
  72. LeCun, Y.; Bengio, Y.; Hinton, G. Deep learning. Nature 2015, 521, 436-444. [CrossRef] [PubMed] · DOI · Paper page
  73. Anonymous. On Evaluating Explainability Algorithms. In Proceedings of the International Conference on Learning Representa- tions, Addis Ababa, Ethiopia, 26-30 April 2020.
  74. Babiker, H.K.B.; Goebel, R. An introduction to deep visual explanation. arXiv 2017, arXiv:1711.09482. · arXiv
  75. Lin, T.Y.; Maire, M.; Belongie, S.; Hays, J.; Perona, P.; Ramanan, D.; Dollár, P.; Zitnick, C.L. Microsoft coco: Common objects in context. In European Conference on Computer Vision, Proceedings of the 13th European Conference, Zurich, Switzerland, 6-12 September 2014; Springer: Cham, Switzerland, 2014.
  76. Babiker, H.K.B.; Goebel, R. Using KL-divergence to focus deep visual explanation. arXiv 2017, arXiv:1711.06431. · arXiv
  77. Zintgraf, L.M.; Cohen, T.S.; Adel, T.; Welling, M. Visualizing deep neural network decisions: Prediction difference analysis. arXiv 2017, arXiv:1702.04595. · arXiv
  78. Robnik-Šikonja, M.; Kononenko, I. Explaining classifications for individual instances. IEEE Trans. Knowl. Data Eng. 2008, 20, 589-600. [CrossRef] · DOI
  79. Smilkov, D.; Thorat, N.; Kim, B.; Viégas, F.; Wattenberg, M. Smoothgrad: removing noise by adding noise. arXiv 2017, arXiv:1706.03825. · arXiv
  80. Sundararajan, M.; Taly, A.; Yan, Q. Axiomatic attribution for deep networks. In Proceedings of the 34th International Conference on Machine Learning, Sydney, Australia, 6-11 August 2017; Volume 70.
  81. Huk Park, D.; Anne Hendricks, L.; Akata, Z.; Rohrbach, A.; Schiele, B.; Darrell, T.; Rohrbach, M. Multimodal explanations: Justifying decisions and pointing to the evidence. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA, 18-23 June 2018.
  82. Hohman, F.; Park, H.; Robinson, C.; Chau, D.H. Summit: Scaling Deep Learning Interpretability by Visualizing Activation and Attribution Summarizations. arXiv 2019, arXiv:1904.02323. · arXiv
  83. Fong, R.C.; Vedaldi, A. Interpretable explanations of black boxes by meaningful perturbation. In Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy, 22-29 October 2017. 104.
  84. Lei, T.; Barzilay, R.; Jaakkola, T. Rationalizing neural predictions. arXiv 2016, arXiv:1606.04155. 105. · arXiv
  85. Fan, F.L.; Xiong, J.; Li, M.; Wang, G. On interpretability of artificial neural networks: A survey. IEEE Trans. Radiat. Plasma Med. Sci. 2021, 5, 741-760. [CrossRef] 115. Fan, F.L.; Xiong, J.; Li, M.; Wang, G. IndependentEvaluation GitHub Code. 2021. Available online: https://github.com/ FengleiFan/IndependentEvaluation (accessed on 7 October 2021). 116. Burkart, N.; Huber, M.F. A Survey on the Explainability of Supervised Machine Learning. J. Artif. Intell. Res. 2021, 70, 245-317. [CrossRef] 117. Agarwal, N.; Das, S. Interpretable Machine Learning Tools: A Survey. In Proceedings of the IEEE Symposium Series on Computational Intelligence (SSCI), Canberra, Australia, 1-4 December 2020; pp. 1528-1534. 118. · DOI · Paper page
  86. Hooker, S.; Erhan, D.; Kindermans, P.J.; Kim, B. Evaluating feature importance estimates. arXiv 2018, arXiv:1806.10758. 119. Yeh, C.K.; Hsieh, C.Y.; Suggala, A.S.; Inouye, D.; Ravikumar, P. How Sensitive are Sensitivity-Based Explanations? arXiv 2019, arXiv:1901.09392. 120. Alexandrov, N. Explainable AI decisions for human-autonomy interactions. In Proceedings of the 17th AIAA Aviation Technology, Integration, and Operations Conference, Denver, CO, USA, 5-9 June 2017. · arXiv

Authors
Buhrmester, V., Münch, D., & Arens, M.
Affiliations
Fraunhofer Institute of Optronics, System Technologies and Image Exploitation (DE)
Corresponding author
Buhrmester, Vanessa
ORCID
Venue
Machine Learning and Knowledge Extraction
Publisher
MDPI AG
Published
2021-12-08
Volume
3
Issue
4
Pages
966-989
ISSN
2504-4990 (ISSN-L)
DOI
10.3390/make3040048
OpenAlex
W2990283237
Cited by
403
References
126
Citations by year
2026: 48 · 2025: 79 · 2024: 101 · 2023: 83 · 2022: 43 · 2021: 34 · 2020: 15
FWCI
22.97
Type
journal article
Language
en
Open access
Licence
Locations
  • Machine Learning and Knowledge Extraction (publishedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • Publikationsdatenbank der Fraunhofer-Gesellschaft (Fraunhofer-Gesellschaft) (submittedVersion, other-oa)
  • Fraunhofer-Publica (Fraunhofer-Gesellschaft) (submittedVersion, cc-by)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
  • Physical Sciences › Computer Science › Software › Software Testing and Debugging Techniques
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
Keywords
explainable AI, deep neural network, black-box models, computer vision, explainers, model interpretability, algorithmic fairness, survey comparison

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review