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.
read more
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.
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
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
Does institutional quality affect CO2 emissions? Evidence from explainable artificial intelligence models
TL;DR: This article showed that effective climate change policies must be associated with improvement in at least three main institutional dimensions: protection of property rights (the rule of law), citizens' participation in elections and freedom of expression (voice), and control of corruption.
25
Rethinking search: making domain experts out of dilettantes
TL;DR: The authors examines how ideas from classical information retrieval and pre-trained language models can be synthesized and evolved into systems that truly deliver on the promise of domain expert advice, but they do not have a true understanding of the world, they are prone to hallucinating, and they are incapable of justifying their utterances by referring to supporting documents in the corpus they were trained over.
25
Mining Cross Features for Financial Credit Risk Assessment
Qiang Liu,Zhaocheng Liu,Haoli Zhang,Yuntian Chen,Jun Zhu +4 more
- 26 Oct 2021
TL;DR: In this paper, a novel automatic feature crossing method called DNN2LR is proposed to find cross features to make simple classifiers to be more accurate without heavy hand-crafted feature engineering.
25
Research on sports fitness management based on blockchain and Internet of Things
Yu Shan,Yuehui Mai +1 more
TL;DR: The actual case test shows that the functions and technical performance indicators of the dynamic fitness management technology can meet the needs of users in indoor and outdoor fitness management, and promote the development of the sports industry and provide a scientific reference.
•Posted Content
Corporate default forecasting with machine learning
TL;DR: The authors analyzed the performance of a set of machine learning (ML) models in predicting default risk, using standard statistical models, such as the logistic regression, as a benchmark, and evaluated the consequences of using an ML-based rating system on the supply of credit and the number of borrowers gaining access to credit.
25
References
I and i
TL;DR: There is, I think, something ethereal about i —the square root of minus one, which seems an odd beast at that time—an intruder hovering on the edge of reality.
38.1K
•Book
C4.5: Programs for Machine Learning
J. Ross Quinlan
- 15 Oct 1992
TL;DR: A complete guide to the C4.5 system as implemented in C for the UNIX environment, which starts from simple core learning methods and shows how they can be elaborated and extended to deal with typical problems such as missing data and over hitting.
27.2K
Classification and regression trees
TL;DR: This article gives an introduction to the subject of classification and regression trees by reviewing some widely available algorithms and comparing their capabilities, strengths, and weakness in two examples.
"Why Should I Trust You?": Explaining the Predictions of Any Classifier
Marco Tulio Ribeiro,Sameer Singh,Carlos Guestrin +2 more
- 13 Aug 2016
TL;DR: In this article, the authors propose LIME, a method to explain models by presenting representative individual predictions and their explanations in a non-redundant way, framing the task as a submodular optimization problem.