In his presentation Bridging AI and Human Expertise at UXPA Boston 2025, Stewart Smith shared insights on designing expert systems that effectively bridge artificial intelligence and human expertise. Here are my notes from his talk:
- Expert systems simulate human expert decision-making to solve complex problems like GPS routing and supply chain planning
- Key components include knowledge base, inference engine, user interface, explanation facility, and knowledge acquisition
- Traditional systems were rule-based, but AI is transforming them with machine learning for pattern recognition
- The explanation facility justifies conclusions by answering "why" and "how" questions
- Trust is the cornerstone of system adoption. if people don't trust your system, they won't use it
- Explainability must be designed into the system from the beginning to trace key decisions
- The "black box problem" occurs when you know inputs and outputs but can't see inner workings
- High-stakes domains like finance or healthcare require greater explainability
- Aim for balance between under-reliance (missed opportunities) and over-reliance (atrophied skills) on AI
- Over-reliance creates false security when users habitually approve system recommendations
- Human experts remain essential for catching bad data feeds or biased data
- Present AI as augmentation to decision-making, not replacement
- Provide confidence scores or indicators of the system's certainty level
- Ensure users can adjust and override AI recommendations where necessary
- Present AI insights within existing workflows that match expert mental models
- Clearly differentiate between human and AI-generated insights
- Training significantly increases AI literacy—people who haven't used AI often underestimate it
- Highlight success stories and provide social proof of AI's benefits
- Focus on automating routine decisions to give people more time for complex tasks
- Trust is the foundation of AI adoption.
- Explainability is a spectrum and must be balanced with performance.
- UX plays a critical role in bridging AI capabilities and human expertise.