Ethical implications of AI in pricing and sales prediction: A theoretical framework

Ravin Kumar; Rajesh Pal; Sanjeev Raghav

doi:10.22271/tpi.2019.v8.i2Sa.25241

Ethical implications of AI in pricing and sales prediction: A theoretical framework

Author(s):
Ravin Kumar, Rajesh Pal and Sanjeev Raghav

Abstract:
As Artificial Intelligence (AI) continues to revolutionize various industries, its integration into pricing and sales prediction processes raises profound ethical considerations. This theoretical framework explores the ethical implications surrounding AI applications in pricing and sales prediction, aiming to provide a comprehensive understanding of the multifaceted challenges posed by this technological advancement. The intersection of AI and pricing strategies introduces complexities that demand careful examination to ensure fair and transparent business practices.

Firstly, the application of AI in pricing introduces concerns related to algorithmic bias. As AI systems rely on historical data to make predictions, they may inadvertently perpetuate existing biases present in the data, leading to discriminatory pricing practices. This framework delves into the mechanisms through which bias can emerge and suggests strategies to mitigate its impact, emphasizing the importance of continuous monitoring and algorithmic transparency.

Secondly, the increased reliance on AI for sales prediction raises questions regarding consumer privacy. The collection and analysis of vast amounts of personal data to inform predictive models create a potential risk of privacy infringement. This framework discusses the ethical considerations surrounding data usage, emphasizing the need for robust privacy safeguards and the development of responsible data management practices within the context of AI-driven sales prediction.

Moreover, the framework explores the economic implications of AI in pricing and sales prediction. The potential for price manipulation, unfair competition, and market concentration poses challenges to market dynamics. Strategies to foster healthy competition and prevent monopolistic practices are examined, highlighting the importance of regulatory frameworks that adapt to the evolving landscape of AI in business.

Lastly, the framework addresses the accountability and transparency of AI algorithms. As AI systems become integral to pricing decisions, ensuring accountability becomes paramount. The paper explores mechanisms for establishing responsibility, proposing guidelines for businesses to uphold transparency and accountability standards.

DOI:
https://doi.org/10.22271/tpi.2019.v8.i2Sa.25241

Pages: 06-09 | 225 Views 137 Downloads

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Vol. 8, Special Issue 2 (2019)

Ethical implications of AI in pricing and sales prediction: A theoretical framework

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