Human-Centered AI and Machine Learning Flashcards
enhances interactive systems by providing smarter, adaptive, and efficient user
interactions
AI
AI can tailor content and interactions based on user preferences and behavior.
Personalization
Applications
like chatbots and voice assistants (e.g., Siri, Alexa)
that understand and respond to human language.
Natural Language Processing (NLP)
AI-powered systems that
recognize and interpret visual inputs for applications
like augmented reality filters and facial recognition.
Computer Vision
Benefits of AI in Interactive Systems
Improved User Experience
Efficiency
Adaptive interfaces that anticipate user needs and behaviors
Improved User Experience
Automation of repetitive tasks and smarter data-driven insights.
Efficiency
Challenges of AI in Interactive system
Privacy Concerns
Bias and Fairness
Making AI decisions understandable for users (e.g., displaying how a recommendation is
made).
Transparency
: Providing users with feedback on how their interactions influence AI-driven
outcomes.
Feedback Mechanisms
Design Principles
Explainability
Control and Autonomy
Error Handling and Recovery
: Interfaces should explain the AI’s actions and provide users with insight into how the system
works.
Explainability
Balance between AI automation and user control. Examples include allowing users
to adjust the level of AI assistance or providing manual overrides.
Control and Autonomy
Designing ways for users to understand and recover from AI errors gracefully
Error Handling and Recovery
: Figma’s AI-based suggestions for layouts and styles
AI-Powered Design Tools
