Explanation
Imagine a doctor making a diagnosis. You wouldn't want them to simply say, "The computer says you have this disease." You'd want to know why the computer made that diagnosis.
Explainable AI (XAI) aims to make AI decision-making more transparent and understandable.
It's about developing AI models that can explain their reasoning, predictions, and actions in a way that humans can comprehend.
Instead of being a black box, XAI provides insights into how an AI system arrived at a particular conclusion.
This involves techniques like feature importance analysis, rule extraction, and visualisation methods that highlight the factors influencing AI decisions.
The goal is to build trust, ensure accountability, and enable humans to effectively collaborate with AI systems.
Examples
Consumer Example
Think about a loan application being rejected by an AI-powered system.
With XAI, the system wouldn't just say "rejected". It would explain why, perhaps highlighting factors like credit score, income level, or debt-to-income ratio.
This allows the applicant to understand the decision and potentially take steps to improve their chances in the future. It is far more fair to show the reasoning for a decision, especially one that can have such great implications.
Business Example
Imagine a marketing team using AI to target advertising.
With XAI, they can understand why the AI is recommending specific ads to certain customer segments. Perhaps it's identifying a correlation between past purchases and online browsing behaviour.
This insight allows the marketing team to refine their strategy, optimise their campaigns, and improve their return on investment. Moreover, it allows them to ensure they are treating their various customers fairly.