Towards ethical pricing fairness enhancement using mean penalty reduction method

Purpose This paper aims to introduce a new method for pre-processing data to reduce bias in pricing models without compromising profit. The authors develop a straightforward yet impactful technique, called Mean Penalty Bias Reduction, which effectively eliminates unfair bias while preserving overall revenue. This method applies a transformation that ensures the revised data meets a desired disparity threshold. This study experiments with this approach to verify that the revised data satisfies the criteria for a widely accepted fairness measure. Design/methodology/approach The paper reviews existing work and identifies an appropriate measure of fairness, known as disparate impact, which is adapted for pricing. A pre processing transformation that reduces disparate impact is formally derived and proved to maintain profitability. The new transformation is then used to develop an algorithm which is evaluated on two data sets: a publicly available insurance data set and a synthetically created airlines data set with introduced bias. The insurance data set offers real-world applicability, and the synthetic airlines data set allows controlled experimentation with known biases, ensuring the robustness of the findings in different scenarios. Findings This paper offers empirical insights into the critical importance of fairness in pricing and the need to ensure that machine learning models are not biased against protected characteristics. It introduces a new methodology that businesses can use to reduce bias in pricing models without compromising profitability. This approach highlights the growing relevance of ethical considerations in today’s world, demonstrating how companies can achieve equitable outcomes while maintaining their financial objectives. Research limitations/implications Because the suggested methodology aims to maintain revenue, it reduces, rather than completely eliminates, bias while removing disparate impact. Naturally, pre-processing methods alone are insufficient; continuous monitoring and assessment are essential to ensuring a model’s fairness over time. Practical implications The practical implication of this methodology is that businesses can achieve a balance between fairness and profitability in their pricing models. By reducing bias and removing disparate impact, companies can foster more equitable customer relationships and comply with regulatory standards. However, because pre-processing methods alone are not enough, businesses must implement ongoing monitoring and assessment to ensure the continued fairness of their models. This approach not only mitigates ethical and legal risks but also enhances the company’s reputation and trustworthiness in the eyes of consumers and stakeholders. Social implications Implementing this methodology has significant social implications by promoting fairness and reducing discrimination in pricing models. By addressing bias against protected characteristics, companies can contribute to greater social equity and inclusion. This approach helps to ensure that all customers are treated fairly, regardless of their background, fostering trust and loyalty. In addition, as businesses adopt fairer practices, societal awareness and demand for ethical standards in machine learning and data usage will likely increase, encouraging a broader cultural shift towards equity and justice in various industries. Ultimately, this contributes to a more just and equitable society. Originality/value This paper addresses a recognized need to explore how fairness in pricing can be identified and reduced without affecting the revenue. By investigating methods to mitigate bias in pricing models, this study contributes valuable insights into achieving equitable outcomes. It provides a framework for understanding and addressing disparities, ensuring that pricing strategies do not unfairly disadvantage any group. This research fills an important gap by offering practical solutions and promoting a fairer approach to pricing, ultimately helping businesses and policymakers create more equitable systems that benefit all stakeholders.