A Survey of Methods for Explaining Black Box Models
Riccardo Guidotti,Anna Monreale,Salvatore Ruggieri,Franco Turini,Fosca Giannotti,Dino Pedreschi +5 more
TL;DR: In this paper, the authors provide a classification of the main problems addressed in the literature with respect to the notion of explanation and the type of black box decision support systems, given a problem definition, a black box type, and a desired explanation, this survey should help the researcher to find the proposals more useful for his own work.
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Abstract: In recent years, many accurate decision support systems have been constructed as black boxes, that is as systems that hide their internal logic to the user. This lack of explanation constitutes both a practical and an ethical issue. The literature reports many approaches aimed at overcoming this crucial weakness, sometimes at the cost of sacrificing accuracy for interpretability. The applications in which black box decision systems can be used are various, and each approach is typically developed to provide a solution for a specific problem and, as a consequence, it explicitly or implicitly delineates its own definition of interpretability and explanation. The aim of this article is to provide a classification of the main problems addressed in the literature with respect to the notion of explanation and the type of black box system. Given a problem definition, a black box type, and a desired explanation, this survey should help the researcher to find the proposals more useful for his own work. The proposed classification of approaches to open black box models should also be useful for putting the many research open questions in perspective.
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Figures
Table 3. Summary of methods for opening and explaining black boxes with respect to the problem faced. 
Fig. 6. Black Box Inspection Problem. 
Table 2. Legend of Table 1. In the following are described the features reported and the abbreviations adopted. ![Fig. 11. Saliency Masks for explanation of deep neural network. (Left) From [108] the elements of the image highlighted. (Right) From [25] the mask and the level of accuracy on the image considering and not considering the learned mask.](/figures/figure11-1-5ao3relmau4d.png)
Fig. 11. Saliency Masks for explanation of deep neural network. (Left) From [108] the elements of the image highlighted. (Right) From [25] the mask and the level of accuracy on the image considering and not considering the learned mask. 
Table 4. Summary of methods for opening and explaining black boxes with respect to the explanator adopted. 
Fig. 9. (Left) Generalizable reverse engineering approach: internal peculiarities of the black box are not exploited to build the comprehensible predictor. (Right) Not Generalizable reverse engineering approach: the comprehensible predictor is the result of a procedure involving internal characteristics of the black box.
![Fig. 11. Saliency Masks for explanation of deep neural network. (Left) From [108] the elements of the image highlighted. (Right) From [25] the mask and the level of accuracy on the image considering and not considering the learned mask.](/figures/figure11-1-5ao3relmau4d.webp)
Citations
Towards Risk-Free Trustworthy Artificial Intelligence: Significance and Requirements
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TL;DR: The review emphasises the need to address trustworthiness in AI systems before their deployment in order to achieve the AI goal for good, and demonstrates how trustworthy AI can be employed to eliminate bias in human resources management systems.
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