Solving the Trust Problem: Steps to Strengthen Confidence in AI-Driven Business Applications

This research explores the intricate dynamics of trust and agency concerning Artificial Intelligence (AI) systems within business application settings. As AI technologies continue to be integrated into various corporate processes, understanding the factors that foster trust in these systems becomes crucial. The primary research question guiding this study is: What steps should corporate executives and software engineers take to improve trust in AI-driven business applications?

To examine this question, the research employs Actor-Network Theory (ANT) as a conceptual framework. ANT allows for an analysis of the relationships and interactions among human and non-human actors within the context of AI implementation. By mapping these networks, the study aims to identify key elements that contribute to or hinder trust in AI systems.

The expected outcomes of this research include actionable recommendations for corporate executives and software engineers to enhance trust in AI applications. This may involve strategies for improving transparency, accountability, and user engagement in the deployment of AI systems. The significance of this research lies in its potential to inform best practices within the fields of Science and Technology Studies (STS) and Engineering, providing insights that may lead to more ethical and effective use of AI technologies in business. Ultimately, this study aims to bridge the gap between technical development and societal acceptance, fostering a more trustworthy AI landscape in corporate environments.

School of Engineering and Applied Science

Bachelor of Science in Computer Science

Technical Advisor: Nada Basit

STS Advisor: Bryn Seabrook

Technical Team Members: Irvine Madenga