Ethical Challenges of Artificial Intelligence in Marketing

Introduction

Artificial intelligence is reshaping the marketing landscape, but it is also evolving ethical controversies. The article uses a literature review to scrutinize the ethical challenges of multiple stakeholders in marketing practices. The author also reveals the tensions and interrelationship among ethical principles to discuss the applicability of the deontological approach as pure principles for AI ethics in marketing.

Discussion

Increasing data availability and intensity, computational power, emotional sensing capabilities, and context awareness of artificial intelligence allow organizations to personalize and customize the offerings in marketing practices. However, discrimination may arise in customer prioritization considering economic and demographic aspects, vulnerable consumer grouping, alienation and targeting. Unequal representation and market share concentration are increasing at the business level due to AI-enabled e-commerce solutions. The article considers relevant scholarly work and determines that AI is reshaping marketing practices from an ethical perspective.

AI presents privacy, explainability and trustworthiness as major ethical issues in most contexts, including marketing. However, there are limitations to papers analyzing ethical principles. Although, general convergences such as justice and fairness, transparency, privacy, responsibility and non-maleficence are analyzed in most scholarly work. These issues and convergences are essential to scrutinize from the customer, company and socio-environment perspective.

The author has scrutinized all the AI issues in five categories: beneficence, non-maleficence, autonomy, justice and explicability for multiple stakeholders. The customers and companies can gain personalized recommendations, persuasive appeals, products and services and marketing mix. AI applications boost consumption and sales in marketing, indirectly contributing to carbon emissions and other environmental issues. Further, the negative externalities are connected to the maleficence principle, which advocates how technology can be misused. According to this principle, AI applications can be maleficence and beneficial simultaneously. Privacy risks can occur during data collection, storage and processing through AI systems. AI algorithms' accuracy and predictive validity increase with massive inputs but can interfere with data privacy and protection. In marketing, errors, biases, mistakes and inaccuracies can lead to false conclusions and biased results. Aside from these, algorithm overreliance, false propositions and inferior recommendations and predictions are significant ethical concerns for customers and businesses. At an individual customer level, autonomy in decision-making is critical to protect AI systems.

In contrast, at the company level, keeping humans in the loop for governance is essential. In future AI systems, faking emotions and psychopaths are common concerns; therefore, human agents must be involved. Delegations of decisions to AI can impact customer autonomy for decision-making.

From the perspective of justice, the results from AI systems and algorithms may contain biases and discrimination, and it can further amplify them in overall decision-making. AI systems can discriminate the customer groups considering emotional, economic and demographical factors in customer targeting and relations management. Such results can indirectly diminish social well-being and good. However, the technology can also detect discrimination and bias in human decisions. At the business level, recommender systems can discriminate against traditional retailers.

Conversely, lack of transparency and accountability are significant concerns related to explicability. It is identified that AI can impact the customer and business decisions with the availability of the information to them. It can further complicate the decision-making with information overload, frustration and irritations. Black box issues are essential to determine the intelligence of the technology.

Conclusion

The author has significantly discussed AI's positive and negative side in marketing for multiple customers. It also presented examples from industries and their outcomes. With an extensive literature review, the author indicates that AI can make faster and more effective decisions. However, human agents must be involved in decisions to control the negative consequences. At the same time, the intelligence of AI cannot be used as a ready-to-use solution in sensitive contexts. Its ethical issues must be overcome through well-trained models and even applications across the industries.

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