Jointly Optimize Positive and Negative Saliencies for Black Box Classifiers

Jung, Hun Soon; Oh, Youngrock; Park, Jeonghyung; Kim, Min Soo

doi:10.1007/978-3-030-68796-0_6

Cited by 1 publication

(1 citation statement)

References 14 publications

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“…It generates individual saliency maps of an image of interest by using other XAI methods such as GradCam, and then it considers the average of these maps. Alternatively, the joint mask method (JMM) [154] integrates two saliency masks to obtain faithful maps that focus on the essential parts of an object whilst removing noises. The first mask highlights the positive region of an input image that maximises the target class probability.…”

Section: Visual Explanations As Salient Masksmentioning

confidence: 99%

Classification of Explainable Artificial Intelligence Methods through Their Output Formats

Vilone

Longo

2021

MAKE

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Machine and deep learning have proven their utility to generate data-driven models with high accuracy and precision. However, their non-linear, complex structures are often difficult to interpret. Consequently, many scholars have developed a plethora of methods to explain their functioning and the logic of their inferences. This systematic review aimed to organise these methods into a hierarchical classification system that builds upon and extends existing taxonomies by adding a significant dimension—the output formats. The reviewed scientific papers were retrieved by conducting an initial search on Google Scholar with the keywords “explainable artificial intelligence”; “explainable machine learning”; and “interpretable machine learning”. A subsequent iterative search was carried out by checking the bibliography of these articles. The addition of the dimension of the explanation format makes the proposed classification system a practical tool for scholars, supporting them to select the most suitable type of explanation format for the problem at hand. Given the wide variety of challenges faced by researchers, the existing XAI methods provide several solutions to meet the requirements that differ considerably between the users, problems and application fields of artificial intelligence (AI). The task of identifying the most appropriate explanation can be daunting, thus the need for a classification system that helps with the selection of methods. This work concludes by critically identifying the limitations of the formats of explanations and by providing recommendations and possible future research directions on how to build a more generally applicable XAI method. Future work should be flexible enough to meet the many requirements posed by the widespread use of AI in several fields, and the new regulations.

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Section: Visual Explanations As Salient Masksmentioning

confidence: 99%