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Explainable AI (XAI) is artificial intelligence that can document how specific outcomes were generated in such a way that ordinary humans can understand the process. The goal of XAI is to make sure that artificial intelligence programs are transparent regarding both the purpose they serve and how they work.
Explainable AI is a common goal and objective for data scientists and machine learning engineers. It is one of the five major principles that characterize trust in AI systems. The others four principles are:
- Resiliency
- Lack of machine bias
- Reproducibility
- Accountability
Explainable artificial intelligence is a key part of applying ethics to AI use in business. The idea behind explainable AI is that AI programs and technologies should not be black box models that people cannot understand.
Explainable AI supports responsible AI by providing an acceptable level of transparency and accountability for decisions made by complex AI systems. This is particularly important when it comes to AI systems that have a significant impact on people’s lives, especially those AI applications and services used in healthcare, finance, human resource management and criminal justice.
Techopedia Explains Explainable AI (XAI)
Explainability and interpretability are often used as synonyms when discussing artificial intelligence in everyday speech, but technically, explainable AI models and interpretable AI models are quite different.
How Does Explainable AI Work?
Non-linearity, complexity and high-dimensional inputs can make an AI model so complicated that it can quickly become impossible for the data scientists and machine learning engineers who design and implement it to understand how their explainable AI model arrived at a decision.
Non-linearity: Some AI models, like deep neural networks, use non-linear functions to produce outputs, which in turn makes their decision-making process non-linear and hard to interpret.
Complexity: AI systems, particularly those based on deep learning, can involve millions of parameters and hyperparameters.
High-dimensional inputs: When applying AI to images, audio or video, the vast number of features used in the decision-making process becomes hard to visualize.
The lack of understanding about how an AI system works is one of the reasons consumers don’t trust AI and why oversight and governance are so important.
To address these challenges, researchers are working on developing methods for explaining complex AI decisions. Popular approaches to making complex AI models explainable include designing AI systems that can self-generate human-readable explanations of their decision-making processes and designing AI systems that are able to provide visualizations of the data and features they use to produce output.
Explainability vs. Interpretability in AI
Explainability and interpretability are often used as synonyms when discussing artificial intelligence in everyday speech, but technically, explainable AI models and interpretable AI models are quite different.
An interpretable AI model makes decisions that can be understood by a human without requiring additional information. Given enough time and data, a human being would be able to replicate the steps that interpretable AI takes to arrive at a decision.
In contrast, an explainable model is so complicated that a human being wouldn’t be able to understand how the model makes a prediction without being given an analogy or some other human-understandable explanation for the model’s decisions. Theoretically, even if given an infinite amount of time and data, a human being would not be able to replicate the steps that explainable AI takes to arrive at a decision.
