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Design robust decision systems
·
Module 8
Engineering Trust in AI Systems
1
System 1 vs. System 2 Thinking in AI Interactions
Distinguish between System 1 (intuitive) and System 2 (deliberate) thinking in the context of user interaction with AI.
2
Communicating AI Confidence in UI
Design a UI component that communicates an AI's confidence level for a recommendation.
3
Designing Transparent AI: Traceability UI Patterns
Design a UI pattern that provides traceability for an AI's reasoning or data sources.
4
Handling Low AI Confidence in UI
Define behavioral shifts in a UI when AI confidence is low (e.g., hedging language, escalation prompts).
5
Detecting Model Drift
Explain model drift and identify user-facing signals that could indicate performance degradation.
6
Data Poisoning: Risk and Detection
Describe data poisoning as a risk and how monitoring can help detect it.
7
AI Health Dashboard: Conceptual Monitoring Views
Design a conceptual monitoring view for an AI Health Dashboard (e.g., drift, anomaly, data freshness metrics).
8
Tracing AI Decisions: From Output to Source Data
Define an accountability chain for an AI-powered decision, from the model output back to the source data.
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