Why Explainable AI matters

Explainable Artificial Intelligence (XAI) Concepts

Folkert Stijnman

ML Engineer

An AI system

Prediction: analyze data, identify patterns, and make forecasts
- Examples: market trends, health risks, and weather patterns
Generation: generate content based on user input
- Examples: text, code, images, and sounds

Explainable AI (XAI)

Gap between technology and comprehension
Transparency
Interpretability
Accountability
Trust in AI

robot and hand touching

Transparency in AI

photo of foggy car window

Transparency in AI: the ability to understand and trace how AI systems make decisions or predictions
Ensures fairness and accuracy
Also ensures AI is justifiable

Interpretability and accountability

Interpretability: the extent to which a human can understand the cause of a decision made by an AI system
Important for high-risk fields such as healthcare
Accountability: refers to assigning responsibility for the actions and decisions made by AI systems

scales of justice inside computer

Building trust in AI systems

black and white traditional elevator

People tend to distrust new automated solutions
Understanding cultivates trust
Relationship between human and technology

Why does XAI matter?

XAI allows AI systems to be:

Effective and justifiable
Clear and understandable
Promote trust and fairness
Nurturing relationship between humans and technology

Let's practice!

Explainable Artificial Intelligence (XAI) Concepts

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