Explainable Artificial Intelligence-Based Competitive Factor Identification

Competitor analysis is an essential component of corporate strategy, providing both offensive and defensive strategic contexts to identify opportunities and threats. The rapid development of social media has recently led to several methodologies and frameworks facilitating competitor analysis through online reviews. Existing studies only focused on detecting comparative sentences in review comments or utilized low-performance models. However, this study proposes a novel approach to identifying the competitive factors using a recent explainable artificial intelligence approach at the comprehensive product feature level. We establish a model to classify the review comments for each corresponding product and evaluate the relevance of each keyword in such comments during the classification process. We then extract and prioritize the keywords and determine their competitiveness based on relevance. Our experiment results show that the proposed method can effectively extract the competitive factors both qualitatively and quantitatively.

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Why to Buy Insurance? An Explainable Artificial Intelligence Approach

Risks ◽

10.3390/risks8040137 ◽

2020 ◽

Vol 8 (4) ◽

pp. 137

Author(s):

Alex Gramegna ◽

Paolo Giudici

Keyword(s):

Artificial Intelligence ◽

Real Time ◽

Life Insurance ◽

Insurance Coverage ◽

Insurance Policy ◽

Explainable Artificial Intelligence ◽

Explainable Ai ◽

Shapley Values ◽

Intelligence Approach ◽

Artificial Intelligence Approach

We propose an Explainable AI model that can be employed in order to explain why a customer buys or abandons a non-life insurance coverage. The method consists in applying similarity clustering to the Shapley values that were obtained from a highly accurate XGBoost predictive classification algorithm. Our proposed method can be embedded into a technologically-based insurance service (Insurtech), allowing to understand, in real time, the factors that most contribute to customers’ decisions, thereby gaining proactive insights on their needs. We prove the validity of our model with an empirical analysis that was conducted on data regarding purchases of insurance micro-policies. Two aspects are investigated: the propensity to buy an insurance policy and the risk of churn of an existing customer. The results from the analysis reveal that customers can be effectively and quickly grouped according to a similar set of characteristics, which can predict their buying or churn behaviour well.

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An explainable artificial intelligence approach for decoding the enhancer histone modifications code and identification of novel enhancers in Drosophila

Genome Biology ◽

10.1186/s13059-021-02532-7 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Jareth C. Wolfe ◽

Liudmila A. Mikheeva ◽

Hani Hagras ◽

Nicolae Radu Zabet

Keyword(s):

Artificial Intelligence ◽

Histone Modifications ◽

Target Genes ◽

Large Set ◽

Coding Regions ◽

Explainable Artificial Intelligence ◽

High Degree ◽

Intelligence Approach ◽

Artificial Intelligence Approach ◽

Insight Into

Abstract Background Enhancers are non-coding regions of the genome that control the activity of target genes. Recent efforts to identify active enhancers experimentally and in silico have proven effective. While these tools can predict the locations of enhancers with a high degree of accuracy, the mechanisms underpinning the activity of enhancers are often unclear. Results Using machine learning (ML) and a rule-based explainable artificial intelligence (XAI) model, we demonstrate that we can predict the location of known enhancers in Drosophila with a high degree of accuracy. Most importantly, we use the rules of the XAI model to provide insight into the underlying combinatorial histone modifications code of enhancers. In addition, we identified a large set of putative enhancers that display the same epigenetic signature as enhancers identified experimentally. These putative enhancers are enriched in nascent transcription, divergent transcription and have 3D contacts with promoters of transcribed genes. However, they display only intermediary enrichment of mediator and cohesin complexes compared to previously characterised active enhancers. We also found that 10–15% of the predicted enhancers display similar characteristics to super enhancers observed in other species. Conclusions Here, we applied an explainable AI model to predict enhancers with high accuracy. Most importantly, we identified that different combinations of epigenetic marks characterise different groups of enhancers. Finally, we discovered a large set of putative enhancers which display similar characteristics with previously characterised active enhancers.

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