Shaping the future of responsible AI

As governments around the world move to put new guardrails on artificial intelligence in 2026, University of Delaware professor Xiao Fang brings a perspective shaped long before AI became a business buzzword.

More than 25 years ago, when few business scholars were studying artificial intelligence, Fang began asking how the technology could be designed to support better decisions - not just faster ones. Today, as organizations face growing pressure to make AI systems transparent, accountable and fair, his research offers practical insight into how AI can be built to serve people and society, not just machines.

A professor of management information systems in UD's Alfred Lerner College of Business and Economics, Fang focuses on "use-inspired AI for business," developing tools that address real-world business and societal challenges while minimizing potential risks. The National Science Foundation distinguishes between foundational AI, which develops general methods independent of application, and use-inspired AI, which is motivated by specific real-world problems.

Fang's work firmly falls into the latter category. His objective is to design AI systems that solve meaningful business and societal problems while carefully considering potential harms. His work spans applications from identifying bias in AI-generated content to building interpretable models for mission-critical decisions like medical diagnosis and financial analysis.

He is a recognized researcher whose work contributes to UD's Top 20 ranking on the Association for Information Systems(AIS) list of high-quality journals over the past three years (2023-2025).

Rather than viewing emerging AI regulations as obstacles to innovation, Fang sees them as objectives that can work in harmony with economic goals, helping organizations design AI systems that are both responsible and effective.

A long view on artificial intelligence

Fang's interest in AI began around 2000 while he was pursuing his doctorate in business. At the time, artificial intelligence was rarely studied in business schools, and research in the area was often misunderstood.

"As a business Ph.D. student, I took graduate-level computer science courses, including artificial intelligence," Fang said. "That exposure, along with my work in data mining, really sparked my interest."

Despite early challenges getting AI-focused work published in business journals, Fang remained committed. Over the past 26 years, he has watched AI evolve from symbolic systems built on explicit rules and logic to today's data-driven models powered by machine learning and neural networks. While the technology has changed dramatically, his core focus has remained steady.

"My research focus has become clearer over time," Fang said. "I work on AI that is driven by real applications and real needs."