An Introduction to XAI
In a world fueled by digital data, the use of artificial intelligence is prolific – from the automation of human processes to discovering hidden insights at scale and speed. But, if you can’t see how AI makes its decisions, how can you trust the results? Consumers want reassurance about ethical use and fairness related to AI. Businesses need to mitigate the risk of unintended consequences when employing these advanced, complex solutions.
The expanding use of even more complex models using deep learning methods for use cases such as facial recognition, voice to text, or TV show recommendations adds urgency to open the AI ‘black box’ and provide transparency.
This is where Explainable AI or XAI comes in.
What exactly is explainability in the world of AI? Think of it as a two-step process – first, interpretability, the ability to interpret an AI model, second, explainability, to be able to explain it in a way humans can comprehend. Explainable models provide transparency — so you can stay accountable to customers, build trust, and make decisions with confidence.
Read this eBook to learn more about:
- What XAI is
- XAI Considerations
- Unintended Data Consequences
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