Stanford Webinar - Human-Centered AI: Designing Systems People Trust

Expand Beyond Traditional User-Centered Design

Human-centered AI must address impacts on non-users such as healthcare patients, resource allocation recipients, and data labelers who are affected by but do not directly interact with systems.

Design Across Three Critical Levels

Effective AI development integrates the user level, community level (indirectly affected groups), and societal level (broad cultural impacts like social media's influence on mental health or news consumption).

Western Cultural Ontologies Dominate AI Models

Research reveals large models default to Western perspectives, such as viewing trees as isolated objects rather than interconnected living systems, thereby limiting representation of Indigenous and non-Western philosophical frameworks.

Sovereign AI Addresses Cultural Control

Countries are pursuing sovereign AI across infrastructure, data, models, applications, and talent to ensure national security, economic independence, and cultural representation independent of dominant Western technologies.

Training Data Reflects Geographic Cultural Bias

Large models train predominantly on North American and Western European internet data, creating systems that may not reflect the values, languages, or ontologies of communities in Iran, Singapore, or Indigenous populations.

AI as Personalized Health Coach

The Bloom application uses motivational interviewing techniques to co-create fitness plans with users, featuring a visual garden interface that grows based on weekly activity levels to encourage sustained engagement.

Ambient Sensing for Aging in Place

Researchers are developing privacy-preserving ambient sensing AI to help older adults live independently at home longer while carefully considering impacts on care workers and family members who interact with the system.