Deep learning algorithms are indispensable tools for achieving human-like performance in artificial intelligence. While the algorithms themselves are difficult to explain, they are essentially used to process inputs and combine them with existing data and probabilities to develop numerical outputs, called scores. These scores are then used to make data-driven decisions.
A primary challenge with deep learning algorithms is the ability of businesses to trust and defend the scores provided by the AI. While deep learning enables businesses to make data-driven decisions, those decisions need to be defensible. The process with which the AI developed the score also needs to be explainable. This is where explainable artificial intelligence comes into play.
To dive into the details of explainability, we first need to define terms. There are two terms often used to discuss the accessibility of AI — explainability and interpretability. While explainable and interpretable machine learning are often used interchangeably, the two terms are slightly different. Simply put, explainability is the extent to which the machine learning model and its algorithms can be explained in human terms. Interpretability, on the other hand, describes the extent to which observers can predict the outcome of the AI model through cause and effect analysis. While both concepts are important to machine learning models, we’ll specifically be covering explainability here.
Explainable deep learning models are difficult due to two key features of AI:
Both of these factors lend to the complicated nature of deep learning AI. This means it is difficult for modelers to know what the AI “sees” in the data, leaving them with less control over the outcome scores and reducing their ability to explain the outcomes. Without explainability, there is no visibility into the process, making it difficult to trust and defend the outcomes produced by the AI.
However, if a modeler can master explainability for an AI deep learning model, they can maximize visibility and adjust the deep learning model to create defensible outputs.
Explainable artificial intelligence is a significant challenge in machine learning today. AI models are often used in a range of applications across a variety of industries, running everything from security cameras and smartphones to legal and financial decisions. Instances of AI developing poor or discriminatory outcomes have also gained widespread attention. Given the rising use and increased skepticism toward AI, explainability is more important than ever.
There are four key benefits driving the need for understanding how deep learning models create their outputs:
There are many ways to increase the explainability of deep learning models. Some of the less mathematically rigorous techniques include:
The true value of deep learning is only achieved when we associate it with explainability. Explanations for model results can affect business outcomes, workflows, and even organizational objectives. Most of all, they indicate how best to adjust models to achieve goals for deploying predictive analytics.
Without explainability, users blindly follow models like they once blindly followed intuition. Explainability gives them the understanding to best apply accurate predictions.
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RazorThink is an AI system company dedicated to simplifying the creation, deployment, and management of AI systems. Our artificial intelligence operating system provides an intuitive AI development environment that allows users to explore data, build models, troubleshoot engines, and run experiments and analytics. By leveraging pre-built and pre-tested code blocks, the RZT aiOS allows users without advanced software engineering skills to build powerful, explainable AI applications within days.Learn more about RazorThink and the RZT aiOS today by reaching out to speak with a representative.