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2024 | Book

Role of Explainable Artificial Intelligence in E-Commerce

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About this book

The technological boom has provided consumers with endless choices, removing the hindrance of time and place. Understanding the dynamic and competitive business environment, marketers know they need to reinforce indestructible customer experience with the support of algorithmic configurations to minimize human intrusion. World Wide Web (WWW) and online marketing have changed the way of conducting business; with artificial intelligence (AI), business houses can furnish a customized experience to fulfil the perceived expectation of the customer.

Artificial intelligence bridges the gap between business and prospective clients, provides enormous amounts of information, prompts grievance redressal system, and further complements the client’s preference. The opportunities online marketing offers with the blend of artificial intelligence tools like chatbots, recommenders, virtual assistance, and interactive voice recognition create improved brand awareness, better customer relationshipmarketing, and personalized product modification.

Explainable AI provides the subsequent arena of human–machine collaboration, which will complement and support marketers and people so that they can make better, faster, and more accurate decisions. According to PwC’s report on Explainable AI(XAI), AI will have $15.7 trillion of opportunity by 2030. However, as AI tools become more advanced, more computations are done in a “black box” that humans can hardly comprehend. But the rise of AI in business for actionable insights also poses the following questions: How can marketers know and trust the reasoning behind why an AI system is making recommendations for action? What are the root causes and steering factors? Thus, transparency, trust, and a good understanding of expected business outcomes are increasingly demanded.

Table of Contents

Frontmatter
Introduction to Explainable AI (XAI) in E-Commerce
Abstract
In the ever-evolving landscape of technology, companies strive for innovation to maintain competitiveness. Artificial Intelligence (AI) has permeated every sector, and in the realm of e-commerce (EC), its impact is notably evident. Various AI applications, such as recommendation systems, fake filters, and fraud detection, have greatly benefited the EC industry. However, a lingering issue is the challenge of understanding and explaining the outcomes generated by AI algorithms, which, in turn, affects their trustworthiness. In addressing this concern, there’s an ongoing discourse regarding the ethics and privacy implications of AI, prompting additional research endeavors. The objective is to enhance the trustworthiness and ethical standing of AI systems. This has led to the resurgence of Explainable AI (XAI), a domain focused on making AI results more comprehensible to users. The prevailing challenge lies in the fact that existing technologies often fall short in providing detailed explanations of how algorithms arrive at specific results or recommendations. Specifically in e-commerce, where decisions often demand immediate action, the integration of XAI systems becomes crucial. These systems aim to provide instant justifications, filling the gap left by current technologies that struggle to offer thorough explanations of the decision-making process behind AI-generated results or recommendations.
Meenu Chaudhary, Loveleen Gaur, Gurinder Singh, Anam Afaq
Explainable Artificial Intelligence (XAI) for Managing Customer Needs in E-Commerce: A Systematic Review
Abstract
Businesses across industries have changed how they operate as a result of the introduction and adoption of technology. Importantly, significant technological advancements in e-commerce try to persuade consumers to purchase particular goods and brands. AI is increasingly used as a vital new tool for personalization and product customization to meet specific needs. It provides insights into the decision-making criteria, elements, and data required to provide a recommendation. The machine learning field known as XAI studies and strives to understand the models and techniques utilized in the black box decisions produced by AI systems. In order to deploy explainable XAI systems, this study suggested that ML models need to be improved in order to make them easier to comprehend and interpret. A branch of machine learning known as XAI studies and aims to understand the models and processes involved in how AI systems make decisions in a “black box.” It offers insights into the considerations, factors, and information needed to generate a suggestion. This study made the recommendation that ML models be enhanced, making them interpretable and understandable, in order to deploy explainable XAI systems. This paper addresses this issue by examining and analyzing recent work in XAI methodologies, needs, principles, applications, and case studies. We introduce a novel XAI approach that facilitates the development of explainable models while maintaining a high level of learning performance.
Koti Tejasvi, V. Lokeshwari Vinya, Jagini Naga Padmaja, Ruqqaiaya Begum, M. A. Jabbar
Decoding the Recommender System: A Comprehensive Guide to Explainable AI in E-commerce
Abstract
The rapid growth of e-commerce has resulted in an increasingly competitive landscape where businesses strive to provide personalized and engaging experiences to their customers. Recommender systems, powered by advanced algorithms and artificial intelligence, are central to this effort, curating tailored suggestions for products, services, and content. However, the complex and opaque decision-making processes of these systems often act as black boxes, limiting user understanding and trust. This chapter delves into the exclusive roles of explainable AI in the decision-making processes of recommender systems within the context of e-commerce, highlighting its importance in fostering trustworthiness, ensuring ethical and legal compliance, and facilitating debugging and model improvement. We explore various types of explanations, techniques for generating explanations, and real-world examples of explainable recommender systems. In conclusion, explainable AI is an indispensable component of recommender systems, playing a critical role in enhancing user trust and engagement, ultimately leading to improved customer satisfaction and increased revenues for e-commerce businesses. As AI systems continue to evolve and become more integrated into our lives, explainability will remain a crucial aspect of their design and implementation.
Garima Sahu, Loveleen Gaur
“The AI Revolution in E-Commerce: Personalization and Predictive Analytics”
Abstract
E-commerce, has totally changed the way we purchase goods and do business. Customers may now browse, purchase, and receive goods all from the comfort of their own homes, thanks to advancements in technology and the expansion of the internet. Over the years, e-commerce has undergone significant transformation, affecting both the retail environment and customer expectations. Integration of artificial intelligence (AI) into the e-commerce sector, especially in the customization domain, is one important component that has made a substantial contribution to this progress. The volume of data produced by online shoppers increased quickly alongside the growth of e-commerce. Traditional techniques of customization could no longer meet the wide range of requests and tastes of consumers. E-commerce customization started to heavily rely on AI algorithms, especially machine learning. Through extensive data analysis, artificial intelligence (AI) has the potential to discern individual user habits, preferences, and behaviors, therefore empowering e-commerce platforms to provide highly customized purchasing experiences. A more customized and relevant buying experience for every client resulted from this, replacing the previous one-size-fits-all strategy. This chapter demonstrates how big data may be used by e-commerce companies to provide highly customized products that meet the demands of their customers and provide them a competitive advantage.
Chitra Krishnan, Jasmine Mariappan
Impact of Artificial Intelligence on Purchase Intention: A Bibliometric Analysis
Abstract
Technology development has brought about new business practices and improved its ability to affect consumer behavior. Technology use also makes it easier to comprehend customer needs and provide better services to them. Therefore, in today's technology-driven marketing scenarios, it is crucial to comprehend the development of artificial intelligence (AI) and how it affects consumers’ purchase intentions. As artificial intelligence (AI) or AI-enabled services become more prevalent in the digital marketplace, we are seeing an impact on consumer purchase intentions. With the aid of bibliometric analysis, this paper seeks to review research on artificial intelligence and how it affects consumer purchase intention. The Web of Science and Scopus databases were used to gather the sample size for the study, which contained 85 papers from the years 2005 through 2023. The study reviews the annual scientific production along with the most prolific authors, articles and journals. Additionally, this study looks at the co-occurrence analysis to identify the thematic clusters utilizing the author's keywords. The annual scientific production analysis reveals a 12.25% yearly growth rate for research publications in the field under study. The top two highly cited journals in the field are the International Journal of Information Management and Telematics and Informatic with 320 and 97 citations respectively. Furthermore, the thematic clusters derived from the co-occurrence network highlight highly used keywords such as artificial intelligence, purchase intention, e-commerce and consumer behavior.
Aatam Parkash Sharma, Naresh Kumar Sharma, Neeru Sidana, Richa Goel
Chatbot-XAI—The New Age Artificial Intelligence Communication Tool for E-Commerce
Abstract
XAI chatbots in e-commerce refer to chatbots that are customized to use natural language processing (NLP) and machine learning (ML) algorithms to recognise and comprehend customer queries and deliver personalized and tailored recommendations and assistance to customers during online shopping. The primary benefit of XAI chatbots in ecommerce is that they can support and clarify their decision-making process and procedures to customers more clearly and helps in building trust and transparency, by decreasing the possibility for errors or biases. Today XAI chatbot can explain why it recommends and proposes a particular product to his customer based on the prior preferences, previous purchases, and browsing history. This chapter aims to provide an overview of how XAI chatbots can be used as a tool in ecommerce to improve customer experience and increase sales. Overall, XAI chatbots have the potential to revolutionize the ecommerce industry however, they must be designed and implemented carefully to ensure they are ethical, secure, and compliant with privacy regulations. The chapter will in detail delve into the technical aspects of XAI chatbots, including the machine learning algorithms and natural language processing techniques used to build them. With the help of detailed pictorial representation, it will exhibit the importance of transparency and interpretability in XAI chatbots with various techniques and approaches of explaining the chatbot's decision-making process to customers. Overall, this chapter will provide a comprehensive overview of XAI chatbots as a tool in ecommerce and their potential impact on the industry.
Kavita Thapliyal, Manjul Thapliyal
Demystifying Applications of Explainable Artificial Intelligence (XAI) in e-Commerce
Abstract
The past ten years have witnessed significant advancements in artificial intelligence (AI), which has led to the widespread implementation of algorithms for the purpose of solving a wide range of issues. Nevertheless, in order to achieve this level of success, model complexity has been increased while at the same time black-box AI models, which lack clearness, have been utilized. With respect to the above mentioned demand, a concept known as XAI is presented with the goal of making AI more understandable and, as a result, accelerating the adoption of AI in important areas. Several relative ideas of XAI prime part in the published article, these difficulties and research ideas over Explainable AI are dispersed despite the fact that they have been identified. This chapter will provide an introduction to XAI, which will describe Why Explainable AI is needed, the various types of perspective groups, the three unique parts that make up XAI, and issues in XAI.
S. Faizal Mukthar Hussain, R. Karthikeyan, M. A. Jabbar
From Algorithms to Ethics: XAI’s Impact on E-Commerce
Abstract
“From Algorithms to Ethics: XAI's Impact on E-Commerce” explores the pivotal role that Explainable Artificial Intelligence plays in transforming the e-commerce landscape. It addresses the ethical challenges arising from algorithmic decision-making and underscores the significance of transparency, fairness, and trust in the world of online retail. As e-commerce continues to shape the future of commerce, XAI emerges as a fundamental enabler of ethical and sustainable digital engagement.
Loveleen Gaur
Metadata
Title
Role of Explainable Artificial Intelligence in E-Commerce
Editors
Loveleen Gaur
Ajith Abraham
Copyright Year
2024
Electronic ISBN
978-3-031-55615-9
Print ISBN
978-3-031-55614-2
DOI
https://doi.org/10.1007/978-3-031-55615-9

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