Addressing Bias in Generative AI: Challenges and Research Opportunities in Information Management

TL;DR

This paper explores the challenges of bias in Large Language Models within information management, proposing a comprehensive framework and research agenda to enhance fairness, transparency, and ethical considerations in AI-driven business systems.

Contribution

It introduces a multidisciplinary framework and identifies key research questions to address bias in LLMs, advancing the field of information management.

Findings

Identifies sources and types of bias in LLMs

Proposes a framework covering stakeholders and ethical considerations

Suggests future research directions for bias mitigation

Abstract

Generative AI technologies, particularly Large Language Models (LLMs), have transformed information management systems but introduced substantial biases that can compromise their effectiveness in informing business decision-making. This challenge presents information management scholars with a unique opportunity to advance the field by identifying and addressing these biases across extensive applications of LLMs. Building on the discussion on bias sources and current methods for detecting and mitigating bias, this paper seeks to identify gaps and opportunities for future research. By incorporating ethical considerations, policy implications, and sociotechnical perspectives, we focus on developing a framework that covers major stakeholders of Generative AI systems, proposing key research questions, and inspiring discussion. Our goal is to provide actionable pathways for researchers to address bias in LLM applications, thereby advancing research in information management that ultimately informs business practices. Our forward-looking framework and research agenda advocate interdisciplinary approaches, innovative methods, dynamic perspectives, and rigorous evaluation to ensure fairness and transparency in Generative AI-driven information systems. We expect this study to serve as a call to action for information management scholars to tackle this critical issue, guiding the improvement of fairness and effectiveness in LLM-based systems for business practice.