Extending Knowledge Graphs with Subjective Influence Networks for Personalized Fashion

Bollacker, Kurt D.; Díaz-Rodríguez, Natalia; Li, Xian

doi:10.1007/978-3-030-00317-3_9

Cited by 9 publications

(10 citation statements)

References 23 publications

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“…Neural-symbolic Systems [297,298,299,300] KB-enhanced Systems [24,169,301,308,309,310] Deep Formulation [264,302,303,304,305] Relational Reasoning [75,312,313,314] Case-base Reasoning [316,317,318] Fig. 11.a) ( Fig.…”

Section: Hybrid Transparent and Black-box Methodsmentioning

confidence: 99%

Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI

et al. 2020

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In the last few years, Artificial Intelligence (AI) has achieved a notable momentum that, if harnessed appropriately, may deliver the best of expectations over many application sectors across the field. For this to occur shortly in Machine Learning, the entire community stands in front of the barrier of explainability, an inherent problem of the latest techniques brought by sub-symbolism (e.g. ensembles or Deep Neural Networks) that were not present in the last hype of AI (namely, expert systems and rule based models). Paradigms underlying this problem fall within the so-called eXplainable AI (XAI) field, which is widely acknowledged as a crucial feature for the practical deployment of AI models. The overview presented in this article examines the existing literature and contributions already done in the field of XAI, including a prospect toward what is yet to be reached. For this purpose we summarize previous efforts made to define explainability in Machine Learning, establishing a novel definition of explainable Machine Learning that covers such prior conceptual propositions with a major focus on the audience for which the explainability is sought. Departing from this definition, we propose and discuss about a taxonomy of recent contributions related to the explainability of different Machine Learning models, including those aimed at explaining Deep Learning methods for which a second dedicated taxonomy is built and examined in detail. This critical literature analysis serves as the motivating background for a series of challenges faced by XAI, such as the interesting crossroads of data fusion and explainability. Our prospects lead toward the concept of Responsible Artificial Intelligence, namely, a methodology for the large-scale implementation of AI methods in real organizations with fairness, model explainability and accountability at its core. Our ultimate goal is to provide newcomers to the field of XAI with a thorough taxonomy that can serve as reference material in order to stimulate future research advances, but also to encourage experts and professionals from other disciplines to embrace the benefits of AI in their activity sectors, without any prior bias for its lack of interpretability.

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Section: Hybrid Transparent and Black-box Methodsmentioning

confidence: 99%

Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI

et al. 2020

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“…When considering the different weighting schemes of SHAP-Backprop, for the PASCAL-Part, the vanilla ResNet classifier baseline performs better than that one for EXPLANet, which means that the part-based classifier EXPLANet is underperforming in this case. It can be explained by several factors, 13 Curated PASCAL-Part Dataset and KG available github.com/ivanDonadello/semantic-PASCAL-Part/. We do not provide a visualization of this KG as it would be unreadable.…”

Section: Results For Pascal-part Datasetmentioning

confidence: 99%

“…Different options exists to leverage a KG as a versatile element to convey explanations [55]. We inspire ourselves by NeSy frameworks for XAI using ontologies and KGs [13,7,18], on explanations of image and tabular data-based models and, more broadly, on the XAI literature [38]. We focused more precisely on attribution methods that try to measure the importance of the different parts of the input toward the output.…”

Section: Knowledge Graphsmentioning

confidence: 99%

EXplainable Neural-Symbolic Learning (X-NeSyL) methodology to fuse deep learning representations with expert knowledge graphs: the MonuMAI cultural heritage use case

Díaz-Rodríguez,

Lamas,

Sanchez

et al. 2021

Preprint

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The latest Deep Learning (DL) models for detection and classification have achieved an unprecedented performance over classical machine learning algorithms. However, DL models are black-box methods hard to debug, interpret, and certify. DL alone cannot provide explanations that can be validated by a non technical audience such as end-users or domain experts. In contrast, symbolic AI systems that convert concepts into rules or symbols -such as knowledge graphs-are easier to explain. However, they present lower generalisation and scaling capabilities. A very important challenge is to fuse DL representations with expert knowledge. One way to address this challenge, as well as the performance-explainability trade-off is by leveraging the best of both streams without obviating domain expert knowledge. In this paper, we tackle such problem by considering the symbolic knowledge is expressed in form of a domain expert knowledge graph. We present the eXplainable Neural-symbolic learning (X-NeSyL) methodology, designed to learn both symbolic and deep representations, together with an explainability metric to assess the level of alignment of machine and human expert explanations. The ultimate objective is to fuse DL representations with expert domain knowledge during the learning process so it serves as a sound basis for explainability. In particular, X-NeSyL methodology involves the concrete use of two notions of explanation, both at inference and training time respectively: 1) EXPLANet: Expert-aligned eXplainable Part-based cLAssifier NETwork Architecture, a compositional convolutional neural network that makes use of symbolic representations, and 2) SHAP-Backprop, an explainable AI-informed training procedure that corrects and guides the DL process to align with such symbolic representations in form of knowledge graphs. We showcase X-NeSyL methodology using MonuMAI dataset for monument facade image classification, and demonstrate that our approach improves not only explainability of DL models but also performance.

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“…In order to assess story quality, word embeddings can be used to estimate cognitive interest [58,59,60]. Fashion styles and its social media tags can be used to predict subjective influence and novelty [61]. Could such influence and novelty metrics correlate with actionable or useful explanations?…”

Section: Challenge 4 Tackling the Lack Of Personal Touch In Technologymentioning

confidence: 99%

Accessible Cultural Heritage through Explainable Artificial Intelligence

Díaz-Rodríguez

Pisoni

2020

Adjunct Publication of the 28th ACM Conference on User Modeling, Adaptation and Personalization

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Ethics Guidelines for Trustworthy AI advocate for AI technology that is, among other things, more inclusive. Explainable AI (XAI) aims at making state of the art opaque models more transparent, and defends AI-based outcomes endorsed with a rationale explanation, i.e., an explanation that has as target the non-technical users. XAI and Responsible AI principles defend the fact that the audience expertise should be included in the evaluation of explainable AI systems. However, AI has not yet reached all public and audiences, some of which may need it the most. One example of domain where accessibility has not much been influenced by the latest AI advances is cultural heritage. We propose including minorities as special user and evaluator of the latest XAI techniques. In order to define catalytic scenarios for collaboration and improved user experience, we pose some challenges and research questions yet to address by the latest AI models likely to be involved in such synergy.

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Extending Knowledge Graphs with Subjective Influence Networks for Personalized Fashion

Cited by 9 publications

References 23 publications

Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI

Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI

EXplainable Neural-Symbolic Learning (X-NeSyL) methodology to fuse deep learning representations with expert knowledge graphs: the MonuMAI cultural heritage use case

Accessible Cultural Heritage through Explainable Artificial Intelligence

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