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This study investigates the intricate relationship between personalisation and automation in Artificial Intelligence (AI), focussing on their impact on human interactions. The purpose is to discern patterns significantly influencing modern society, using notable examples from e-commerce, social media and digital advertising. The research employs a multifaceted approach, drawing insights from real-world examples of AI implementation. Noteworthy instances include Amazon's use of AI algorithms for personalised product recommendations, Netflix's application of AI in content recommendations and Tesla's Full Self-Driving feature in autonomous vehicles. The findings reveal the dual nature of personalisation and automation. In e-commerce, personalised recommendations, such as those on Amazon, can lead to impulse buying and potential financial strain. Similarly, social media algorithms, like Facebook's echo chamber and advertising strategies, exemplified by Google's ‘skippable’ and ‘non-skippable’ ads, strategically influence user behaviour and decision-making. The research also highlights the success of Netflix's personalised content delivery and the potential safety challenges in Tesla's Full Self-Driving feature. The study underscores the importance of a balanced approach to personalisation and automation, especially in ethical considerations, user privacy and data security.

In the era of generative artificial intelligence (AI), big data analytics, business analytics and mega global digital corporations, the profession of marketing is at a crossroads between ‘Prosumer-Marketing’ and ‘Market Grooming’. Whereas prosumer (producers + consumers) marketing means a process of exchange in which producers and consumers have equal, just, control, voluntary, fully aware engagement and control over the process of design, development and exchange of goods, services and values. On the other hand, ‘Market Grooming’ is a one-sided, unethical process of conditioning or influencing, deceiving, or persuading or manipulating and even exploiting customers by the marketing organisations, without customers' voluntary consent, permissions, awareness, etc. As the consumers have asymmetric access to information, asymmetric and lesser favourable levels of control, and lesser power in the process of exchange, as customers trust the marketers or are dependent on popular brands, the markers tend to exploit the situation. The process of market grooming has become easier due to the power of AI, generative AI, ChatGPT, TikToketing, machine learning and big data analytics leading to the development of sophisticated predictive models and persuasive models. This chapter explores and analyses a range of techniques in marketing such as permission marketing, flywheel marketing, subliminal marketing, neuromarketing, cyberstalking, ethical marketing, etc., in the era of AI. The arguments for high concerns pertaining to potential market grooming are supported by theories of ethics, theories of digital marketing and models of AI. The chapter concludes with some strategic recommendations.

Artificial intelligence (AI) has transformed the field of hiring, enabling employers to collect and analyse massive amounts of data to understand and predict the suitability of candidates. However, AI can also have subconscious effects on candidates’ and employers needs through biased data, which can stem from human biases, algorithmic errors or external factors. For example, Amazon scrapped an AI-based recruitment programme that favoured male candidates over female candidates due to the historical patterns in the resumes it analysed. This paper examines how AI can shape candidate's needs through biased data from various sources and types, and what are the consequences for candidate's welfare and rights. We review the literature on AI applications in hiring, the origins and kinds of bias in AI systems, and the potential risks and benefits for candidates. We also suggest some guidelines for reducing bias in AI and enabling candidates to make informed and ethical choices online. We argue that AI can be a double-edged sword for candidate's needs and that more research and regulation are required to ensure its fair and accountable use.

The concept of ‘intelligence’ used to differ between human and machines, until the disruption of artificial intelligence (AI). The field of AI is advancing far more rapidly than the establishment of rules and regulations, which is causing certain fear. However, slowing down this progression to avoid economic crisis is not an option because of open-source AI, which facilitates faster development processes and collective contributions to codes and algorithms. Public policies, such as the ‘European Union AI Act (EU AI)’, ‘Whitehouse AI’, and the G7's ‘Hiroshima Artificial Intelligence Process’ (HAP), are already drafted. Regulators need to adopt a dynamic approach given AI's rapid advancement, and they need to eventually strive for international harmonisation in their rules and regulations for better collaborations. The EU's AI Act is the ‘world's first comprehensive law’ and it focuses on five main pillars similar to other countries drafts: ensuring AI usage is safe, transparent, traceable, non-discriminatory and environmentally friendly. They portray four risk categories against which citizens can file complaints: (1) Unacceptable risk (2) High risk (3) Generative AI (4) Limited risk. The US AI policies include ‘The Blueprint for an AI Bill of Rights: Making Automated Systems Work for the American People’ and the ‘Executive Order on Safe, Secure, and Trustworthy Artificial Intelligence’. This conceptual study extensively reviews the concept of AI and compares pioneering draft laws while providing recommendations on ethics and responsible AI. The contribution of this study is that it sheds light on the evolving evolution of AI and the challenges posed by the rapid advancement of AI technology, emphasising the necessity for flexible and adaptive regulatory frameworks. This is the first paper to explore AI from the academic and political perspective.

Banking traces back to 2000 BC in Assyria, India and Sumeria. Merchants used to give grain loans to farmers and traders to carry goods between cities. In ancient Greece and Roman Empire, lenders in temples, provided loans, and accepted deposits while performed change of money. The archaeological evidence uncovered in India and China corroborates this. The major development in banking came predominantly in the mediaeval, Renaissance Italy, with the major cities Florence, Venice and Genoa being the financial centres. Technology has become an inherent and integral part of our lives. We are generating a huge amount of data in transfer, storage and usage, with greater demands of ubiquitous accessibility, inducing an enormous impact on industry and society. With the emergence of smarter cities and societies, the security challenges pertinent to data become greater, impending impact on the consumer protection and security. The aim of this chapter is to highlight if SSI and passwordless authentication using FIDO-2 protocol assuage security concerns such as authentication and authorisation while preserving the individual's privacy.

This research, titled ‘AI in Digital Marketing: The Ethical Implications of Digital Influence on Markets and Consumers,’ conducts a comprehensive examination of the nuanced role played by artificial intelligence (AI) in shaping consumer behaviour and influencing decision-making processes. While the incorporation of AI in marketing offers commendable advantages, such as providing personalised content and optimising strategies to enhance customer experiences and market efficiency, it concurrently introduces ethical considerations. This study meticulously scrutinises the latent potential for market grooming, wherein AI subtly guides consumers towards decisions that may not align with their best interests. By delving into instances of data privacy breaches, algorithmic biases and the unintended consequences of hyper-personalisation, this research contributes substantively to the ongoing discourse on the responsible utilisation of AI. The study underscores the imperative need for regulatory frameworks aimed at ensuring ethical practices in the dynamically shifting digital landscape. It endeavours to strike an equitable balance between the constructive contributions and potential pitfalls of AI in the realm of marketing. Through this research, we aim to shed light on the ethical dimensions associated with the digital manipulation of markets and consumers, providing insights that can inform industry practices, policymaking and public awareness.

A Digital Twin (DT) is a digital replica of an artefact which is updated on real-time or semi–real-time basis. In 2017, Gartner listed DT as one of the top 10 emerging technologies of the year. Since then, there have been numerous attempts to develop architecture and reference models for DTs, and in some studies, DT construction for real-world case studies is reported. This chapter attempts to provide a contextualised background on DT for smart cities. It also discusses various stakeholders involved in devising and/or employing DTs in a smart city. The chapter concludes with a set of recommendations for the training requirements of final DT users.

The changing environment and competitive market forces have brought many changes in the business sector that have put organisations under immense pressure. The use of psychometric assessments and behavioural profiling help organisations to determine individuals' abilities, aptitudes, personality traits, values and factors which intrinsically motivate them and assist in bringing the right people on board who fit well within the organisational culture and can contribute towards the performance goals. Although behavioural profiling and psychometric assessments are accepted worldwide, however, developing countries particularly the public sector still relies on conventional recruitment methods and the adaptation of contemporary behavioural profiling and psychometric assessments is a challenge. Therefore, this chapter evaluates how the adaptation of behavioural profiling and psychometric assessments in the civil service exams in developing countries can improve the selection process and ultimately can help to improve the quality of public services, capacity building and achieving sustainability goals.