Explainable Artificial Intelligence (XAI) Concepts
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Learn how explainable AI makes model decisions transparent, how SHAP and LIME interpret black box models, and how to design AI with end users in mind.
Explainable AI (XAI) addresses a growing problem: many AI models reach decisions that are hard to follow, which makes those decisions difficult to trust or justify. The lessons look at transparency, interpretability and accountability, the trade-off between a model’s accuracy and how easily it can be explained, and how SHAP and LIME shed light on black box models. They also consider why large language models are hard to explain and how to build explainability into a system at the start.
What You Will Achieve
- Decide whether a question calls for an explanation of one prediction or of a model’s overall behavior
- Weigh a model’s predictive power against how easily its decisions can be explained
- Pitch an explanation of an AI system at the right level for technical and non-technical listeners
- Spot what goes wrong when end users are left out of an AI project, and plan for their needs during design
Program Overview
Foundations of Explainable AI
Why explainability matters, what separates transparency from interpretability, and how to talk about AI with different groups of people.
- Goals of XAI and scenarios that need interpretability
- Transparency versus interpretability
- Balancing accuracy and simplicity
- Audience segmentation and communication
XAI Techniques for Black Box Models
Ways to classify explanation methods, and the tools used to interpret models whose inner workings are hidden.
- Local and global explanations
- Model-specific and model-agnostic methods, with regression and classification analogies
- SHAP and LIME
- Explainability and reasoning of LLMs
Applying XAI by Design
Building explainability into AI systems from the outset, shown through case studies and the needs of different industries.
- Tailoring explanations to the end user
- Case studies in job applicant screening and online learning
- Industry requirements and trust in AI systems
- Cross-disciplinary work, proactive deployment and the future of XAI
Certificate Awarded (Paid)
Job Outlook
Explainability matters wherever AI decisions affect people, such as hiring and online learning, and in work that involves building, reviewing or explaining AI systems to colleagues and end users.
Good for: Managers, analysts and other non-coders who need to discuss, question or explain AI decisions without building models themselves.
Keep in mind: It stays conceptual: SHAP and LIME are introduced as ideas, so applying them to your own models needs separate hands-on practice.
Bottom line: A plain-language tour of XAI that gives end users, large language models and industry case studies as much attention as the explanation methods.
Frequently Asked Questions
What are SHAP and LIME used for in this course?
The second chapter introduces both as model-agnostic tools for interpreting black box models, and one lesson weighs when to use SHAP, LIME or both. They are presented as concepts within the wider set of explanation methods.
Does it deal with large language models?
Yes. The techniques chapter closes with lessons on understanding LLMs and their reasoning, and on why the complexity of these models makes their decision-making hard to make transparent.
Which real-world examples does the course use?
The final chapter works through case studies on screening job applicants and on online learning, then looks at how XAI requirements differ between industries and what leads users to trust an AI system.
What does XAI by design mean here?
It refers to building explainability into an AI system from the outset, with the end user's needs in view. The lessons cover the risks of leaving end users out and then apply the approach to a worked scenario.
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