Responsible AI: Principles and Practices/

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Introduction to Explainable AI

Introduction to Explainable AI

Get an introduction to Explainable AI concepts.

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Explainable AI, or XAI, is a tool that helps people understand and trust what a computer program powered by artificial intelligence is doing. Imagine you have a machine that can predict things, like whether you’ll get a loan or not. XAI helps make those predictions easier to understand. When we can’t understand or make sense of something, we usually don’t trust it. XAI is here to make sure we can trust the predictions made by AI. We use AI to make decisions that are fair and not influenced by personal biases. But these decisions can be risky if we can’t explain or understand them.

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Key questions for explainability
Key questions for explainability

Why is Explainable AI important?

Explainable AI (XAI) plays a pivotal role in bridging the gap between artificial intelligence and business outcomes. It facilitates seamless communication between data science teams and nontechnical executives, fostering a shared understanding of product capabilities and limitations and ultimately enhancing governance.

Below, we look at the significance of Explainable AI:

Significance of XAI

Dimension

Significance




Accountability

  • It ensures accountability when models make erroneous or unexpected decisions.


  • It identifies contributing factors behind such erroneous outcomes, which is essential for preventing recurring issues. It provides greater control over AI tools.




Trust

  • In high-stakes domains like healthcare and finance, trust is paramount. 


  • Stakeholders must fully comprehend the model’s operations before embracing and trusting its decisions. XAI provides the means to deliver that evidence.





Compliance

  • Explainability is a necessity for auditors to uphold compliance with policies.


  • Global regulations mandate transparent communication of automated decision-making logic and consequences, making XAI indispensable.



Performance

  • XAI can enhance model performance by offering insights into its inner workings.


  • This understanding enables precise fine-tuning and optimization, ensuring that models operate at their peak efficiency.



Enhanced control

  • Comprehension of the model’s decision-making process exposes previously unidentified vulnerabilities and flaws. 


  • It enables organizations to gain greater control and the ability to swiftly rectify errors.

Let’s look at some practical examples to explain why it matters:

  • Medical diagnoses: Imagine a ...