Course Description
In these lessons on Transparency and Explainability in Artificial Intelligence, you’ll learn about explainability and interpretability and its intersection with the concepts of black box and glass box AI. You’ll review the advantages and disadvantages of open-source software in the development of an AI system. You’ll also learn about the principles of notification of decision and interaction, and their importance with systems that use natural language or facial recognition.
The principle of transparency suggests that effective oversight of AI systems should be possible. These lessons will develop your understanding of the concept of transparency in decision systems, including different levels of opacity, key concepts around transparency including explainability, interpretability, black box and glass box AI using human-in-the-loop systems.
Explainability is one of the principles for AI ethics. Explainability is a key factor that allows humans to analyze the functioning of an AI system. This section will introduce you to the concepts of black box and glass box AI, and how these models can be used to analyze and explain AI decision-making. You’ll also gain insight into how a black box model is used in the context of interpretability.
You’ll review concepts of transparency, error checking, democratization and access as each relates to open-source software. Then, you’ll discover the principles of notification of decision and interaction, and their importance when interacting with systems using natural language, such as chatbots, or facial recognition being used in public.
What You'll Learn
- Describe the principle of transparency and its relationship to black box and glass box systems
- Identify why explainability is important in AI
- Explain the advantages and disadvantages of open-source software in AI development
- Distinguish explainability from interpretability and their intersection with black box and glass box AI
- Recognize the principles of notification of decision and interaction for systems using natural language or facial recognition
- Examine transparency, error checking, democratization, and access as they relate to open-source software
Key Takeaways
- The principle of transparency suggests that effective oversight of AI systems should be possible.
- Explainability is one of the principles for AI ethics and is a key factor that allows humans to analyze the functioning of an AI system.
- Transparency in decision systems involves different levels of opacity and key concepts such as explainability, interpretability, and black box and glass box AI using human-in-the-loop systems.
- Black box and glass box models can be used to analyze and explain AI decision-making, and a black box model is used in the context of interpretability.
- Notification of decision and interaction matters when interacting with systems using natural language, such as chatbots, or facial recognition used in public.
Frequently Asked Questions
What topics does this course cover?
It covers transparency and explainability in AI, including explainability and interpretability, black box and glass box AI, the advantages and disadvantages of open-source software, and the principles of notification of decision and interaction.
What will I learn about open-source software in this course?
You will review the advantages and disadvantages of open-source software in developing an AI system, including the concepts of transparency, error checking, democratization, and access as each relates to open-source software.
How does the course address natural language and facial recognition systems?
It covers the principles of notification of decision and interaction and their importance when interacting with systems using natural language, such as chatbots, or facial recognition being used in public.
What lessons are included in this course?
The lessons are Transparency and Oversight; Explainability and Interpretability; Accountability in Black Box and Glass Box; Open Source and the Value of Common Algorithms; and Notification of Decision and Interaction.
What skills does this course help develop?
It develops skills in Artificial Intelligence Systems, Artificial Intelligence, Artificial Intelligence Development, Explainable AI (XAI), and Transparency in Human-Computer Interaction.
