In the latest blog post on the “nuts and bolts” of AI, Cimplifi discusses the transparency, explainability and interpretability of AI!

Their post titled (wait for it!) Transparency, Explainability, and Interpretability of AI (available here) discusses how people don’t understand the “why” and “how” of AI, so they consider the technology to be a “black box”, which causes hesitancy for its use. Understanding the “why” and “how” ties to three concepts: transparency, explainability, and interpretability.

Many use the term “transparency” to refer to the ability to understand the “why” and “how” of the AI algorithm, but transparency is only one concept of understanding AI. Here are definitions of transparency, explainability, and interpretability:

Transparency refers to the openness in the design, development, and deployment of AI systems. A transparent AI system is one where its mechanisms, data sources, and decision-making processes are openly available and understandable.

Explainability involves the ability to describe in understandable terms how the AI system reached a specific decision or output. Explainability is more about the logic or reasoning behind individual AI decisions, making the AI’s processes accessible and relatable to end-users.

Interpretability goes deeper than explainability, focusing on the inner workings of the algorithm. It’s about understanding the model’s decision-making process on a detailed level, often requiring technical insight into the AI’s functioning.

So, what are examples of of transparency, explainability, and interpretability? And what are the challenges associated with them? Find out here, it’s only one click! I hope I’m being transparent enough when I say you should check it out! 😉

So, what do you think? Are you excited to learn about the nuts and bolts of AI? Please share any comments you might have or if you’d like to know more about a particular topic.

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