Explainable AI (XAI)
Definition: This means building artificial intelligence systems that can clearly show how and why they make decisions, similar to a student showing their work on a math test.
In other words, it ensures AI is not a “black box.” If an AI tool decides whether someone should get a loan or predicts the outcome of a legal case, explainable AI makes it possible to ask “Why?” and actually get an answer that people can understand.
Example
Imagine an AI program that helps judges decide bail. If it recommends that someone should not be released, explainable AI would let the judge see the reasons, such as the person’s past record or risk factors. This makes the decision more transparent and easier to trust.
Why it matters?
Lawyers need to trust the tools they use. If an AI suggests a case strategy or ranks evidence, you should know why. Explainability helps lawyers verify accuracy, avoid bias, and meet professional responsibility standards. It builds trust with clients and ensures decisions are defensible if ever challenged in court.
How is it different than Algorithmic Transparency?
Explainable AI is closely related to algorithmic transparency, but they are not the same. Explainable AI focuses on helping people understand why an AI system made a specific decision. Algorithmic transparency focuses on showing how the system works behind the scenes, including its data and structure. Explainability helps interpret outcomes, while transparency helps evaluate the system itself.
Analogy:
Transparency is like being able to pop the hood and see the engine so that you can inspect how it’s built.
Explainability is like having a dashboard that tells you what the car is doing right now to understand why it’s turning, braking, or accelerating.
Learn more: Opening the Black Box and Making the Case for Explainable AI