14 research outputs found
Explainable product backorder prediction exploiting CNN: Introducing explainable models in businesses
Due to expected positive impacts on business, the application of artificial intelligence has been widely increased. The decision-making procedures of those models are often complex and not easily understandable to the company’s stakeholders, i.e. the people having to follow up on recommendations or try to understand automated decisions of a system. This opaqueness and black-box nature might hinder adoption, as users struggle to make sense and trust the predictions of AI models. Recent research on eXplainable Artificial Intelligence (XAI) focused mainly on explaining the models to AI experts with the purpose of debugging and improving the performance of the models. In this article, we explore how such systems could be made explainable to the stakeholders. For doing so, we propose a new convolutional neural network (CNN)-based explainable predictive model for product backorder prediction in inventory management. Backorders are orders that customers place for products that are currently not in stock. The company now takes the risk to produce or acquire the backordered products while in the meantime, customers can cancel their orders if that takes too long, leaving the company with unsold items in their inventory. Hence, for their strategic inventory management, companies need to make decisions based on assumptions. Our argument is that these tasks can be improved by offering explanations for AI recommendations. Hence, our research investigates how such explanations could be provided, employing Shapley additive explanations to explain the overall models’ priority in decision-making. Besides that, we introduce locally interpretable surrogate models that can explain any individual prediction of a model. The experimental results demonstrate effectiveness in predicting backorders in terms of standard evaluation metrics and outperform known related works with AUC 0.9489. Our approach demonstrates how current limitations of predictive technologies can be addressed in the business domain
Arabic Sentiment Analysis with Noisy Deep Explainable Model
Sentiment Analysis (SA) is an indispensable task for many real-world
applications. Compared to limited resourced languages (i.e., Arabic, Bengali),
most of the research on SA are conducted for high resourced languages (i.e.,
English, Chinese). Moreover, the reasons behind any prediction of the Arabic
sentiment analysis methods exploiting advanced artificial intelligence
(AI)-based approaches are like black-box - quite difficult to understand. This
paper proposes an explainable sentiment classification framework for the Arabic
language by introducing a noise layer on Bi-Directional Long Short-Term Memory
(BiLSTM) and Convolutional Neural Networks (CNN)-BiLSTM models that overcome
over-fitting problem. The proposed framework can explain specific predictions
by training a local surrogate explainable model to understand why a particular
sentiment (positive or negative) is being predicted. We carried out experiments
on public benchmark Arabic SA datasets. The results concluded that adding noise
layers improves the performance in sentiment analysis for the Arabic language
by reducing overfitting and our method outperformed some known state-of-the-art
methods. In addition, the introduced explainability with noise layer could make
the model more transparent and accountable and hence help adopting AI-enabled
system in practice.Comment: This is the pre-print version of our accepted paper at the 7th
International Conference on Natural Language Processing and Information
Retrieval~(ACM NLPIR'2023
Textual Entailment Recognition with Semantic Features from Empirical Text Representation
Textual entailment recognition is one of the basic natural language
understanding(NLU) tasks. Understanding the meaning of sentences is a
prerequisite before applying any natural language processing(NLP) techniques to
automatically recognize the textual entailment. A text entails a hypothesis if
and only if the true value of the hypothesis follows the text. Classical
approaches generally utilize the feature value of each word from word embedding
to represent the sentences. In this paper, we propose a novel approach to
identifying the textual entailment relationship between text and hypothesis,
thereby introducing a new semantic feature focusing on empirical
threshold-based semantic text representation. We employ an element-wise
Manhattan distance vector-based feature that can identify the semantic
entailment relationship between the text-hypothesis pair. We carried out
several experiments on a benchmark entailment classification(SICK-RTE) dataset.
We train several machine learning(ML) algorithms applying both semantic and
lexical features to classify the text-hypothesis pair as entailment, neutral,
or contradiction. Our empirical sentence representation technique enriches the
semantic information of the texts and hypotheses found to be more efficient
than the classical ones. In the end, our approach significantly outperforms
known methods in understanding the meaning of the sentences for the textual
entailment classification task.Comment: Pre-print for our paper at International Conference on Speech &
Language Technology for Low-resource Languages (SPELLL'2022
Unveiling Black-boxes: Explainable Deep Learning Models for Patent Classification
Recent technological advancements have led to a large number of patents in a
diverse range of domains, making it challenging for human experts to analyze
and manage. State-of-the-art methods for multi-label patent classification rely
on deep neural networks (DNNs), which are complex and often considered
black-boxes due to their opaque decision-making processes. In this paper, we
propose a novel deep explainable patent classification framework by introducing
layer-wise relevance propagation (LRP) to provide human-understandable
explanations for predictions. We train several DNN models, including Bi-LSTM,
CNN, and CNN-BiLSTM, and propagate the predictions backward from the output
layer up to the input layer of the model to identify the relevance of words for
individual predictions. Considering the relevance score, we then generate
explanations by visualizing relevant words for the predicted patent class.
Experimental results on two datasets comprising two-million patent texts
demonstrate high performance in terms of various evaluation measures. The
explanations generated for each prediction highlight important relevant words
that align with the predicted class, making the prediction more understandable.
Explainable systems have the potential to facilitate the adoption of complex
AI-enabled methods for patent classification in real-world applications.Comment: This is the pre-print of the submitted manuscript on the World
Conference on eXplainable Artificial Intelligence (xAI2023), Lisbon,
Portugal. The published manuscript can be found here
https://doi.org/10.1007/978-3-031-44067-0_2
From Large Language Models to Knowledge Graphs for Biomarker Discovery in Cancer
Domain experts often rely on most recent knowledge for apprehending and
disseminating specific biological processes that help them design strategies
for developing prevention and therapeutic decision-making in various disease
scenarios. A challenging scenarios for artificial intelligence (AI) is using
biomedical data (e.g., texts, imaging, omics, and clinical) to provide
diagnosis and treatment recommendations for cancerous conditions.~Data and
knowledge about biomedical entities like cancer, drugs, genes, proteins, and
their mechanism is spread across structured (knowledge bases (KBs)) and
unstructured (e.g., scientific articles) sources. A large-scale knowledge graph
(KG) can be constructed by integrating and extracting facts about semantically
interrelated entities and relations. Such a KG not only allows exploration and
question answering (QA) but also enables domain experts to deduce new
knowledge. However, exploring and querying large-scale KGs is tedious for
non-domain users due to their lack of understanding of the data assets and
semantic technologies. In this paper, we develop a domain KG to leverage
cancer-specific biomarker discovery and interactive QA. For this, we
constructed a domain ontology called OncoNet Ontology (ONO), which enables
semantic reasoning for validating gene-disease (different types of cancer)
relations. The KG is further enriched by harmonizing the ONO, metadata,
controlled vocabularies, and biomedical concepts from scientific articles by
employing BioBERT- and SciBERT-based information extractors. Further, since the
biomedical domain is evolving, where new findings often replace old ones,
without having access to up-to-date scientific findings, there is a high chance
an AI system exhibits concept drift while providing diagnosis and treatment.
Therefore, we fine-tune the KG using large language models (LLMs) based on more
recent articles and KBs.Comment: arXiv admin note: substantial text overlap with arXiv:2302.0473
Explainable AI for Bioinformatics: Methods, Tools, and Applications
Artificial intelligence(AI) systems based on deep neural networks (DNNs) and
machine learning (ML) algorithms are increasingly used to solve critical
problems in bioinformatics, biomedical informatics, and precision medicine.
However, complex DNN or ML models that are unavoidably opaque and perceived as
black-box methods, may not be able to explain why and how they make certain
decisions. Such black-box models are difficult to comprehend not only for
targeted users and decision-makers but also for AI developers. Besides, in
sensitive areas like healthcare, explainability and accountability are not only
desirable properties of AI but also legal requirements -- especially when AI
may have significant impacts on human lives. Explainable artificial
intelligence (XAI) is an emerging field that aims to mitigate the opaqueness of
black-box models and make it possible to interpret how AI systems make their
decisions with transparency. An interpretable ML model can explain how it makes
predictions and which factors affect the model's outcomes. The majority of
state-of-the-art interpretable ML methods have been developed in a
domain-agnostic way and originate from computer vision, automated reasoning, or
even statistics. Many of these methods cannot be directly applied to
bioinformatics problems, without prior customization, extension, and domain
adoption. In this paper, we discuss the importance of explainability with a
focus on bioinformatics. We analyse and comprehensively overview of
model-specific and model-agnostic interpretable ML methods and tools. Via
several case studies covering bioimaging, cancer genomics, and biomedical text
mining, we show how bioinformatics research could benefit from XAI methods and
how they could help improve decision fairness
Peeking Inside the Schufa Blackbox: Explaining the German Housing Scoring System
Explainable Artificial Intelligence is a concept aimed at making complex
algorithms transparent to users through a uniform solution. Researchers have
highlighted the importance of integrating domain specific contexts to develop
explanations tailored to end users. In this study, we focus on the Schufa
housing scoring system in Germany and investigate how users information needs
and expectations for explanations vary based on their roles. Using the
speculative design approach, we asked business information students to imagine
user interfaces that provide housing credit score explanations from the
perspectives of both tenants and landlords. Our preliminary findings suggest
that although there are general needs that apply to all users, there are also
conflicting needs that depend on the practical realities of their roles and how
credit scores affect them. We contribute to Human centered XAI research by
proposing future research directions that examine users explanatory needs
considering their roles and agencies.Comment: 7 pages, 3 figures, ACM CHI 2023 Workshop on Human-Centered
Explainable AI (HCXAI
Re-ranking the Search Results for Users with Time-periodic Intents
This paper investigates the time of search as a feature to improve the personalization of information retrieval systems. In general, users issue small and ambiguous queries, which can refer to different topics of interest. Although personalized information retrieval systems take care of user’s topics of interest, but they do not consider if the topics are time periodic. The same ranked list cannot satisfy user search intents every time. This paper proposes a solution to rerank the search results for time sensitive ambiguous queries. An algorithm "HighTime" is presented here to disambiguate the time sensitive ambiguous queries and re-rank the default Google results by using a time sensitive user profile. The algorithm is evaluated by using two comparative measures, MAP and NDCG.
Results from user experiments showed that re-ranking of search results based on HighTime is effective in presenting relevant results to the users