Shaping the future of responsible AI - University of Delaware

2026-03-18 · Source: artifical intelligence via Google News · Field: Business & Management — Corporate Strategy & Leadership, Project & Product Management, Operations & Process Management · Depth: Intermediate, medium

Summary

University of Delaware Professor Xiao Fang, a management information systems expert at the Alfred Lerner College of Business and Economics, focuses on "use-inspired AI for business," developing tools to address real-world challenges while minimizing risks. His research, spanning over 25 years, predates AI's current business prominence and emphasizes designing AI systems for transparency, accountability, and fairness. Fang's work, recognized by UD's Top 20 ranking on the AIS list, includes identifying bias in AI-generated content and building interpretable models for critical applications like medical diagnosis and financial analysis. He views emerging AI regulations, such as those taking shape in 2026, not as obstacles but as objectives that can align with economic goals, fostering trust and sustainable success. His projects include an AI-based industry classification system and an interpretable AI model for depression diagnosis, both designed for direct business application and explainability.

Key takeaway

For AI Product Managers evaluating new AI deployments, prioritize systems designed with inherent transparency and accountability. Your focus should be on integrating social objectives like fairness and explainability directly into the AI's design framework, rather than treating regulations as afterthoughts. This approach not only ensures compliance with evolving global regulations taking shape in 2026 but also builds essential trust, which is critical for the long-term success and adoption of your AI products.

Key insights

Responsible AI design, integrating social objectives with economic goals, builds trust and ensures sustainable business success.

Principles

• Align economic and social objectives in AI design.
• Embed responsible design from the outset.
• Transparency and explainability are critical for trust.

Method

Develop "use-inspired AI" by designing systems that solve specific business/societal problems while explicitly considering and mitigating potential harms, such as bias and lack of interpretability.

In practice

• Use AI for automated industry classification.
• Develop interpretable models for high-stakes decisions.
• Identify and mitigate bias in generative AI content.

Topics

• Responsible AI
• Use-Inspired AI
• AI Bias Detection
• Interpretable AI
• AI Regulation

Best for: CTO, VP of Engineering/Data, Executive, AI Product Manager, Director of AI/ML, AI Ethicist

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Editorial summary, takeaway, and curation by AIssential. Original article published by artifical intelligence via Google News.