Dinesh Kumar

Purpose Artificial intelligence (AI) has sparked interest in various areas, including marketing. However, this exhilaration is being tempered by growing concerns about the moral and legal implications of using AI in marketing. Although previous research has revealed various ethical and legal issues, such as algorithmic discrimination and data privacy, there are no definitive answers. This paper aims to fill this gap by investigating AI’s ethical and legal concerns in marketing and suggesting feasible solutions. Design/methodology/approach The paper synthesises information from academic articles, industry reports, case studies and legal documents through a thematic literature review. A qualitative analysis approach categorises and interprets ethical and legal challenges and proposes potential solutions. Findings The findings of this paper raise concerns about ethical and legal challenges related to AI in the marketing area. Ethical concerns related to discrimination, bias, manipulation, job displacement, absence of social interaction, cybersecurity, unintended consequences, environmental impact, privacy and legal issues such as consumer security, responsibility, liability, brand protection, competition law, agreements, data protection, consumer protection and intellectual property rights are discussed in the paper, and their potential solutions are discussed. Research limitations/implications Notwithstanding the interesting insights gathered from this investigation of the ethical and legal consequences of AI in marketing, it is important to recognise the limits of this research. Initially, the focus of this study is confined to a review of the most important ethical and legal issues pertaining to AI in marketing. Additional possible repercussions, such as those associated with intellectual property, contracts and licencing, should be investigated more deeply in future studies. Despite the fact that this study gives various answers and best practices for tackling the stated ethical and legal concerns, the viability and efficacy of these solutions may differ depending on the context and industry. Thus, more research and case studies are required to evaluate the applicability and efficacy of these solutions in other circumstances. This research is mostly based on a literature review and may not represent the experiences or opinions of all stakeholders engaged in AI-powered marketing. Further study might involve interviews or surveys with marketing professionals, customers and other key stakeholders to offer a full knowledge of the practical difficulties and solutions. Because of the rapid speed of technical progress, AI’s ethical and regulatory ramifications in marketing are continually increasing. Consequently, this work should be a springboard for more research and continuing conversations on this subject. Practical implications This study’s findings have several practical implications for marketing professionals. Emphasising openness and explainability: Marketing professionals should prioritise transparency in their use of AI, ensuring that customers are fully informed about data collection and utilisation for targeted advertising. By promoting openness and explainability, marketers can foster customer trust and avoid the negative consequences of a lack of transparency. Establishing ethical guidelines: Marketing professionals need to develop ethical rules for the creation and implementation of AI-powered marketing strategies. Adhering to ethical principles ensures compliance with legal norms and aligns with the organisation’s values and ideals. Investing in bias detection tools and privacy-enhancing technology: To mitigate risks associated with AI in marketing, marketers should allocate resources to develop and implement bias detection tools and privacy-enhancing technology. These tools can identify and address biases in AI algorithms, safeguard consumer privacy and extract valuable insights from consumer data. Social implications This study’s social implications emphasise the need for a comprehensive approach to address the ethical and legal challenges of AI in marketing. This includes adopting a responsible innovation framework, promoting ethical leadership, using ethical decision-making frameworks and conducting multidisciplinary research. By incorporating these approaches, marketers can navigate the complexities of AI in marketing responsibly, foster an ethical organisational culture, make informed ethical decisions and develop effective solutions. Such practices promote public trust, ensure equitable distribution of benefits and risk, and mitigate potential negative social consequences associated with AI in marketing. Originality/value To the best of the authors’ knowledge, this paper is among the first to explore potential solutions comprehensively. This paper provides a nuanced understanding of the challenges by using a multidisciplinary framework and synthesising various sources. It contributes valuable insights for academia and industry.

Steganography is the foremost influential approach to hide data. Images serve as the most appropriate cover media for steganography. This paper intends to do a performance evaluation of color images and its comparison with the recently proposed approaches, using the modified technique already proposed for grayscale images, by the authors. This approach hides large data in color image using the blocking concept. The blocking process is applied on approximation coefficients of secret image and detail coefficients of red, green and blue components of cover image. The blocks of detail coefficients are replaced with approximation coefficients of secret image using root mean square error method. The key is used to store the position of best matching blocks. It is being predicated that the work will be able to hide large data in a single image. The stego image has better visual quality based on the peak signal to noise ratio values.

Introduction: Professionalism is emphasized in medical school curricula; however, there is lack of consensus on what constitutes professionalism. This study aimed at exploring incoming medical students’ views on professionalism through student constructs of the ‘good’ and the ‘bad’ doctor. Methods: After Institutional Ethics Committee approval and informed consent, all incoming first-year medical students were asked their reasons for pursuing medicine and their perceptions of the attributes of a ‘good’ and a ‘bad’ doctor. Following the anonymous survey, we used nominal group discussions to achieve consensus about the desirable and undesirable attributes that emerged from student responses. After an iterative theming process, a list of themes were derived from the written reflections of students. Results: Competence, communication, and professionalism were some of the perceived attributes of a good doctor, while being money-minded, having inadequate knowledge or competence, and rudeness were some of the reported attributes of a bad doctor. We identified six themes for the question 'Why pursue medicine?': affective / humanitarian response; thoughts about further pursuits; fulfillment; traits of the profession; ability to contribute to society; and personal experiences. Conclusion: Our results suggest that first year medical students have a fair idea of the behavior expected from a good doctor. Formal reflection on these expectations may hone the concept of professionalism for new entrants into the profession. The teaching of professionalism could incorporate such activities to allow students and educators to articulate and explore tensions between what is taught and what is experienced as they progress through the profession.

The last few decades have witnessed the emergence of the field of bioethics. Students entering medical college face unique ethically charged situations. Cadaveric dissection is one such area of ethical uncertainty. Studies have shown that an appropriate orientation towards the values inherent in a doctor's relationship with patients, colleagues, and society might help medical students learn how to navigate ethical dilemmas they face. The aim of this article is to examine the feasibility of incorporating ethics teaching in anatomy education, and to emphasize the crucial role of professional identity in shaping the inner image of the “to-be” physician. We also discuss ways by which professional identity can be emphasized right from the 1st-year of medical school. Such endeavors may help learners find ways to respond ethically in their professional life. Understanding the role of bioethics in anatomy education with regard to professional identity formation can guide policy-makers and medical educationists.

The development of an effective and efficient face recognition system has always been a challenging task for researchers. In a face recognition system, feature selection is one of the most vital processes to achieve maximum accuracy by removing irrelevant and superfluous data. Many optimization techniques, such as particle swarm optimization, genetic algorithm, ant colony optimization, etc., have been implemented in face recognition systems mainly based on two feature extraction methods: discrete cosine transform and principal component analysis. In this research, a nature-inspired well-known algorithm, namely cuckoo search, has been implemented for face recognition. Further, a hybrid method consisting of DCT and PCA is applied to extract the various features by which recognition can be made with a high rate of accuracy. To validate the proposed methodology, the results are also compared with the existing methodologies, such as PSO, differential evolution, and GA.