Mitigating Bias in AI-Driven Business Models: An Ethical Framework

Abstract

Purpose

The increasing adoption of artificial intelligence (AI) in business models has enhanced data-driven decision-making but has also raised significant ethical concerns, including bias, accountability, fairness, transparency and data privacy. This study aims to develop and empirically test a framework for bias mitigation in AI-driven business environments.

Design/methodology/approach

Data were collected from 485 respondents across multiple industries and weighted based on AI prevalence, ethical complexity, risk exposure and regulatory scrutiny. Structural path analysis was conducted using ADANCO to examine the relationships among transparency, accountability, bias mitigation practices, fairness, data privacy and the development of trustworthy AI systems.

Findings

The results indicate that transparency positively influences trust in AI systems; however, excessive transparency may lead to perceptions of unfairness. Accountability mechanisms are essential for responsible AI governance, although overly stringent measures can diminish confidence. Bias mitigation efforts significantly enhance fairness and inclusivity, but when perceived as excessive, they may negatively affect trust. Data privacy protections are critical for safeguarding information, yet they may also constrain openness and collaboration.

Practical implications

The findings highlight the need for a balanced and context-sensitive approach to ethical AI design, enabling organizations to manage ethical trade-offs while ensuring trust, regulatory compliance and sustainable innovation.