Algorithmic Bias in Customer-Facing Decision-Making: Value-Based Optimization for Better Business Results

Abstract

This research project investigates the impact of algorithmic bias in customer-facing decision-making on business results.
Businesses increasingly employ algorithms to facilitate customer-facing decisionmaking within automated, more efficient, reliable, and consistent automated processes.
However, a growing body of evidence has demonstrated that these algorithms can perpetuate existing biases in the foundational data or even produce novel biases stemming from deficiencies in their programming logic. Such biases can result in suboptimal decisionmaking representations, yielding outcomes frequently regarded as inequitable or unethical.
This research explores the potential negative impact of such a bias on business results, the customer attitude towards algorithmic bias, and how a value-based optimization and management of algorithms in customer-facing applications can enhance customer perception, foster trust, and improve retention while either augmenting or preserving the algorithms' efficacy. The study aims to contribute to formulating business guidelines for developing and managing algorithms characterized by fairness, transparency, and the absence of bias.
Methodologically, the research will utilize a comprehensive literature review, a consumer survey, and a conceptual study/simulation design. The anticipated outcomes include directives for preventing and mitigating algorithmic bias and overarching guidelines for effective business management of algorithmic applications.