Ethical Decision-Making Using Artificial Intelligence (eBook)

Sapna Juneja, Rajesh Kumar Dhanaraj, Abhinav Juneja, Malathy Sathyamoorthy, Asadullah Shaikh (Herausgeber)

eBook Download: EPUB

2025
589 Seiten
Wiley-Scrivener (Verlag)
978-1-394-27529-8 (ISBN)

Infos

Ethical Decision-Making Using Artificial Intelligence: Challenges, Solutions, and Applications gives invaluable insights into the ethical complexities of artificial intelligence, empowering the navigation of critical decisions that shape our future in an era where AI's influence on society is rapidly expanding.

The significant impact of artificial intelligence on society cannot be overstated in a time of lightning-fast technical development and growing integration of AI into our daily lives. A new frontier of human potential has emerged with the development and application of AI technologies, pushing the limits of what is possible in the areas of innovation and efficiency. AI systems are increasingly trusted with complicated decisions that affect our security, well-being, and the fundamental foundation of our societies as they develop in intelligence and autonomy. These choices have substantial repercussions for both individuals and communities in a wide range of fields, including healthcare, finance, criminal justice, and transportation. The necessity for moral direction and deliberate decision-making procedures is critical as AI systems develop and become more independent.

Ethical Decision-Making Using Artificial Intelligence: Challenges, Solutions, and Applications examines the complex relationship between artificial intelligence and the moral principles that guide its application. This book addresses fundamental concerns surrounding AI ethics, namely what moral standards ought to direct the creation and use of AI systems. In order to promote responsible AI development that is consistent with human values and goals, this book's goal is to equip readers with the knowledge and skills they need to traverse the ethical landscape of AI decision-making.

Sapna Juneja, PhD is a professor and Associate Dean of Research and Development in the Department of Computer Science and Engineering with the KIET Group of Institutions, with over 17 years of experience. She has published six patents and various research articles in renowned journals. Her research interests include software engineering, computer networks, operating systems, database management systems, and artificial intelligence.

Rajesh Kumar Dhanaraj, PhD is a professor at Symbiosis International University. He has authored and edited over 50 books, numerous book chapters, and over 100 articles in refereed international journals, in addition to 21 patents. His research interests include machine learning, cyber-physical systems, and wireless sensor networks.

Abhinav Juneja, PhD is a professor and the Head of the Department of Computer Science and Information Technology with the KIET Group of Institutions, with over 21 years of teaching experience. He has edited two books and has over 55 publications in books, journals, and conferences. His research focuses on machine learning and Internet of Things.

Malathy Sathyamoorthy, PhD is an assistant professor in the Department of Information Technology at the KPR Institute of Engineering and Technology. She has published over 25 research papers in international journals. 22 papers in international conferences, two patents, four book chapters, and one book. His research interests include wireless sensor networks, networking, security, and machine learning.

Asadullah Shaikh, PhD is a professor, the Head of Research and Graduate Studies, and the coordinator for seminars and training with the College of Computer Science and Information Systems, at Najran University. He has over 170 publications in international journals and conferences. His research interests include Unified Modeling Language model verification and class diagrams verification with Object Constraint Language constraints for complex models, formal verification, and feedback techniques for unsatisfiable UML and OCL class diagrams.

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Standards, Policies, Ethical Guidelines and Governance in Artificial Intelligence: Insights on the Financial Sector

Purohit S.1 and Arora, R.2*

1Symbiosis Institute of Business Management, Nagpur, Symbiosis International University, Maharashtra India

2University School of Business-MBA, Chandigarh University, Punjab, India

Abstract

The financial sector has significantly benefited from the use of Chatbots for maintaining relationships with clients. The financial service chatbots offer personalized and fast assistance to clients by making use of artificial intelligence and natural language processing and are capable of helping with transaction details to deal with queries related to the accounts. Through automation and quick assistance, these financial chatbots improve the communication process and improve engagement. Thus, a large volume of queries can be handled without human intervention, and therefore the operational efficiency of financial institutions is improved and the costs are reduced. While financial chatbots seem promisingly advantageous, they present challenges related to ethical concerns in the processing of financial data or bias. It is therefore pertinent for financial institutions to take security and privacy measures and take steps to mitigate bias while deploying chatbots for financial service assistance. With the development of technology, the integration of artificial intelligence and service chatbots would probably increase in the financial service sector for enhanced customer experience. Therefore, in this chapter, we explore the pivotal issue of mitigation of bias related to the financial service chatbots. For this purpose, we reviewed the existing literature, examined the practical and realworld impacts of AI-related bias, and described the strategies for mitigation of bias in the financial service sector. By showcasing the importance of ethical considerations by the financial firms for building investor trust, we contribute to the shaping of a fair, transparent, and inclusive use of AI-powered chatbots. For bias mitigation, a robust system is required that requires the involvement of varied stakeholders such as design experts, policymakers, and industry stakeholders.

Keywords: Chatbots, financial sector, technology, artificial intelligence (AI), customer service and bias mitigation

1.1 Introduction

Artificial intelligence (AI) has emerged as one of the prime technologies for digital metamorphosis in financial service firms [1]. The evolution of AI and its applications has transfigured the service delivery to the customers of financial service sector firms and their client-relationship management systems [2]. AI plays a vital role in financial service firms by assisting in risk assessment, market analysis and investment decision making [3]. AI has been deployed by many financial service firms for customer support because the AI-based agents can offer a superior customer experience at a lower cost [4]. The deployment of AI has redefined how financial service firms interact and engage with their clients. From among the various AI-based agents such as text-, voice- and image-based, text-based chatbots have gained wide popularity among firms [5–10]. One of the reasons for the popularity of the text-based chatbots [11] is the high use of message apps by millennials, the generation born between 1980 and 2000 [12]. Millennials prefer to chat to reduce direct conversations [13]. Chatbots have been highly accepted among businesses due to their capabilities that can improve customer satisfaction [14, 15]. AI-based service chatbots have been deployed by many firms to offer customer services [16–21]. Through AI-powered financial service chatbots, these firms have streamlined their customer service operations [21] and improved the efficiency and accessibility of financial services [22]. The financial service chatbots can deal with a range of customer queries and can offer quick and personalized assistance [23]. While the financial service chatbots offer a wide range of benefits to financial firms and customers, their use has posed challenges related to the ethics and potential presence of bias in their responses to investors [24–32]. Hence, in this chapter, we investigate and describe the mitigation of bias related to the deployment of AI-powered financial service chatbots. Mitigation of AI-related bias is critical to the building of trust among investors and the promotion of fairness and inclusion within the complex financial service sector. If the responses of financial service chatbots are biased, issues of trust, transparency and social disparities may emerge, leading to mistrust among stakeholders and clients.

One of the reasons the issue of bias is critical to the financial service industry is that the customers are from diverse backgrounds [33]. While on the one hand, chatbots can offer the advantages of streamlined customer interactions and timely information, the algorithmic design can lead to inherited biases, posing challenges to customer services [34–38]. These biases can be related to gender stereotypes, racial prejudices, and socioeconomic assumptions that can impact the nature of responses thus de-benefiting certain user groups [39]. Comprehending and addressing the issue of bias in financial chatbots is not only a technical issue but also a matter of ethical concern [40]. Finance-related decisions significantly impact individuals, and businesses and biased interactions can result in unequal information access and discrimination and thus affect trust in financial relationships.

Therefore, in this chapter, we present an overarching scenario of financial service chatbot bias. A detailed background of bias in AI is provided in this chapter with a focus on chatbots used by financial institutions for customer care. We also explore the real-world consequences of biased interactions with customers, financial institutions, and broader societal perceptions. We explore the ethical issues associated with the usage of chatbots in finance and the transparency required to maintain user trust. We further assess some of the techniques and strategies for the detection and mitigation of bias in financial chatbots. Finally, we discuss the ethical considerations for the deployment of chatbots in finance.

1.2 Chatbots in the Financial Industry

A chatbot is also called a machine conversational agent; it is a software that uses natural language to interact with users [41]. Chatbots can be classified primarily as rule-based and self-learning chatbots [42]. Rule-based chatbots are based on keywords identified through previous customer interactions. However, as these are limited to specific keywords, customers have to look for options or a human agent for the queries that are beyond the keywords [43]. Self-learning chatbots are based on trained datasets and can answer questions beyond the predefined keywords. Therefore, these are heavily based on conversational datasets, machine learning or artificial intelligence for training [44].

Due to the advancement of chatbot technologies [45] and expansion of financial services chatbots are significantly utilized in varied domains such as financial services. These have been applied to areas such as personal advertising [46], financial services [1], banking [47] and more. Prior studies have evaluated the effect of use of chatbots on customer satisfaction and found that positive customer satisfaction depends on the accuracy and credibility of information given by the chatbots [48]. Chatbots have also been used in healthcare [49, 50], the learning domain [51, 52], tourism [53, 54] and others.

Among the various use applications of chatbots in industry the most common are automated customer service [55], assisted self-service technology or for the support of human agents [56–59]. Additionally, they are used to support the preferred non-face-to-face interactions of millennials [60].

The chatbots are generally expected to be quick and accurate in their response for a better customer service. With the advancement in AI technology, chatbots can make use of natural language processing to increase the accuracy of their response. AI chatbots can provide varied services like handling financial queries, offering quick responses. These virtual financial assistants can provide personalized recommendations, guide customers through complex financial processes, and offer valuable insights to help them make informed decisions about their money by leveraging a pool of knowledge data base and related real-time data. It is therefore pertinent to develop efficient and accurate algorithms for training of chatbots. To this end, prior scholars have developed and proposed algorithms to improve chatbot learning for a higher accuracy of response. Further, a study on human vs. chatbot agents has revealed varied outcomes with respect to the accuracy and quality of responses during interactions [61–63].

The financial industry is considered to be a pioneer in the use of chatbots and other AI applications [64]. The burgeoning number of financial firms strive to improve their customer services for a competitive advantage [39, 40]. It was difficult for financial services industry customers to find accurate information on products, processes and systems [65]. It...

Produkt-Details

• Erscheint lt. Verlag: 15.7.2025
• Sprache: englisch
• Themenwelt: Mathematik / Informatik ► Informatik ► Theorie / Studium
• ISBN-10: 1-394-27529-3 / 1394275293
• ISBN-13: 978-1-394-27529-8 / 9781394275298