Ethical Decision-Making Using Artificial Intelligence
Description
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.
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Persons
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.
Content
Preface xxi
1 Standards, Policies, Ethical Guidelines and Governance in Artificial Intelligence: Insights on the Financial Sector 1
Purohit S. and Arora, R.
1.1 Introduction 2
1.2 Chatbots in the Financial Industry 3
1.3 Background of the Study 5
1.4 Literature Review 6
1.5 Understanding Bias in Customer Service Chatbots 8
1.6 Impact of Bias in Financial Chatbot Interactions 10
1.7 Strategies for Mitigating Bias in Financial Customer Service Chatbots 11
1.8 Ethical Considerations and Transparency in Financial Chatbot Firms 13
1.9 Future Directions and Recommendations 15
1.10 Conclusion 16
2 Domain-Specific AI Algorithms and Models in Decision-Making: An Overview 27
P. Kanaga Priya, A. Reethika, Malathy Sathyamoorthy and Rajesh Kumar Dhanaraj
2.1 Introduction 28
2.2 Understanding Domain-Specific Decision Making 36
2.3 Building Blocks of AI for Decision-Making 38
2.4 Domain-Specific AI: Revolutionizing Industries 39
2.5 Ethical and Societal Implications 51
2.6 Future Directions and Emerging Trends 51
2.7 Conclusion 52
3 Role of AI in Decision-Making - A Comprehensive Study 55
Rohit Vashisht, Sonia Deshmukh and Ashima Arya
3.1 Introduction 56
3.2 Need of AI-Based Decision-Making System 58
3.3 Major Obstacle for AI-Based Decision-Making System 62
3.4 Applications of AI-Based Decision-Making System 65
3.5 Case Study: AIDMS for Age-Related Macular Degeneration (amd) 70
3.6 Conclusion and Future Directions 75
4 Ethical Challenges in AI Decision]Making: From the User's Perspective 79
M. Nalini, S. Sandhya and S. Shiwani
4.1 Introduction 80
4.2 Public Perception towards AI 85
4.3 Ethical Dilemmas of AI 87
4.4 Emerging Issues that are Prevailing in the Current World 90
4.5 Future Considerations 95
5 Ethical Decision-Making in Yoga Posture Detection through AI: Fostering Responsible Technology Integration 99
Ishita Jain, Riya Srivastava, Vanshita Srivastava, Vanshika Sinha and Abhinav Juneja
5.1 Introduction 100
5.2 Literature Review 111
5.3 Technologies Used 112
5.4 Dataset Used 115
5.5 Methodology 117
5.6 Conclusion 119
6 Ethical AI: A Design of an Integrated Framework towards Intelligent Decision-Making in Stock Control 125
Mini Verma and Palak Gupta
6.1 Introduction 126
6.2 Benefits and Impact of AI on Inventory Control 128
6.3 Best Practices for Implementing AI for Stock Management in E-Commerce 131
6.4 Formulation of Proposed Model 138
6.5 Conclusion 148
7 Integrating Machine Learning and Data Ethics: Frameworks for Intelligent Ethical Decision-Making 153
Karishma Sharma, Deepa Gupta, Mukul Gupta and Rajesh Dhanaraj
7.1 Introduction 154
7.2 Concept of Machine Learning and Data Ethics 155
7.3 Importance of ML and AI in Design Making 157
7.4 Defining an Intelligent Decision-Making Support System 158
7.5 Transformation of the Decision-Making System to Intelligent Decision-Making Support 159
7.6 Architecture Framework 161
7.7 Conceptual Framework 162
7.8 Cloud-Based Scalability with Auto Scaling 170
7.9 Case Study of Complex Problem Using Framework 174
7.10 Algorithm and Coding Analysis 174
7.11 Results and Impact Analysis 178
7.12 Conclusion 178
8 Importance of Human Loop in AI-Based Decision-Making: Strengthening the Ethical Perspective 183
A. Reethika, P. Kanaga Priya, Malathy Sathyamoorthy and Rajesh Kumar Dhanaraj
8.1 Introduction 184
8.2 Human Interaction with AI Platform 186
8.3 Human and Machine Ethical Annotation 187
8.4 Exploring AI with Human-in-the-Loop Technique 191
8.5 Creating Ethical AI Using HTIL Technique 195
8.6 Conclusion 203
9 AI in Finance and Business: Novel Method for Human Resource Recommendation Using Improved Gradient Boosting Tree Model 207
Mahima Shanker Pandey, Abhishek Singh, Bihari Nandan Pandey, Aparna Sharma and Prashant Upadhyay
9.1 Introduction 208
9.2 Literature Review 210
9.3 The Proposed Model 217
9.4 Evaluation of the Impact of the Technology 218
9.5 Conclusion 222
10 Comprehensive View from Ethics to AI Ethics: With Multifaceted Dimensions 227
Kanika Budhiraja, Gurminder Kaur, Yatu Rani and Rupam Jha
10.1 Introduction 228
10.2 AI (Artificial Intelligence) 230
10.3 Concept of Ethics 234
10.4 AI Ethics 239
10.5 AI Ethics in Business 245
10.6 AI Ethics in Medicine 250
10.7 AI Ethics in Education 254
10.8 Conclusion 255
11 Case Study on Soil Identification for Insecticides and Fertilizer Recommendation Using IoT and Deep Learning: An Ethical Approach in Smart Agriculture 4.0 259
Richa Singh and Rekha Kashyap
11.1 Introduction 260
11.2 Literature Survey 264
11.3 Problem Formulation 268
11.4 Proposed Work 269
11.5 Result and Discussion 271
11.6 Conclusion 276
12 Case Study on Ethical AI-Based Decision-Making in E-Commerce Industrial Sector: Insights on McDonald's and Deliveroo 283
Anushka Singh, Naman Tyagi and Dolly Sharma
12.1 Introduction 284
12.2 Foundations of AutoML 284
12.3 Benefits and Challenges 286
12.4 Industrial Applications of AutoML: McDonald's 289
12.5 Industrial Applications of AutoML: Deliveroo 295
12.6 Ethical Considerations 303
12.7 Future Trends 306
12.8 Conclusion 309
13 AI Insights: Navigating Education News Ethically Through Aggregation and Sentiment Analysis 313
Anshumaan Garg and Dolly Sharma
13.1 Introduction 314
13.2 Literature Review 323
13.3 Methodology 328
13.4 Results Discussion 334
13.5 Conclusion and Future Work 339
14 Case Study on AI-Based Ethical Decision-Making for Smart Transportation 343
S. Muthu Lakshmi, K. Mythili, Malathy Sathyamoorthy, Rajesh Kumar Dhanaraj and Aanjan Kumar S.
14.1 Introduction 344
14.2 Artificial Intelligence 345
14.3 Role of Artificial Intelligence in Transportation 347
14.4 Literature Review 348
14.5 Challenges 351
14.6 AI Ethics 351
14.7 Data Confidentiality and Security 360
14.8 Vision from Data: Smart Decision-Making in Transportation 361
14.9 Conclusions 363
14.10 Future Directions 363
15 Case Study on AI-Based Decision-Making in E-Commerce: Exploring Location-Based Insights for Analysis of Geospatial Data 367
Ashima Arya, Daksh Rampal, Ekagra, Kashish Varshney, Rohit Vashisht and Yonis Gulzar
15.1 Introduction 368
15.2 Objective 372
15.3 Background Knowledge 372
15.4 Related Work 374
15.5 Data Analysis of Geolocation Data 378
15.6 Proposed Methodology 380
15.7 Results 384
15.8 Conclusion 387
15.9 Future 387
Acknowledgment 388
References 388
Index 393
1
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...
