As deep learning technologies like Artificial Intelligence (AI) and Machine learning (ML) advance, we are being challenged to understand the outputs produced by computer algorithms. For example, how did ML algorithms produce a particular result?
Explainable AI (or XAI) covers the processes and tools that enable human users to comprehend the outputs generated by ML algorithms. Organizations must build their trust in AI models when putting them into production.
The entire XAI process is also referred to as a “black box” model that is created directly from the generated data. Next, let’s look at some of the use cases of Explainable AI.
Here are some of the real-life use cases of Explainable AI:
For Natural Language Text:
XAI for Text focuses on developing black-box models for text-related tasks. For example, the text summarization of legal documents. In this use case, users can explore and understand XAI for Text based on the following considerations:
Type of text-focused task under consideration
Explanation techniques being used for the task
The target users for the particular XAI technique
Similarly, an XAI-based deep learning model can classify textual data in the form of reviews and transcripts. Using explainable AI, you can determine why the model predicts based on the specific keywords and phrases included in the text.
You can also use XAI for Text to train a deep-learning model to generate an article summary based on the source text. For instance, you can obtain a distribution of attention scores over selected tokens in the source text. Words (with an attention score between 0-1) are highlighted in the source text and displayed to the end users. The higher the attention score, the darker the text highlighting – and the more is the importance of the word in the article summary.
For Visual Images:
Explainable AI is also used to automate decision-making based on high-resolution visual images. Some examples of high-resolution images include satellite images and medical data. Besides the high volume of satellite data, the captured data is high-resolution and contains multiple spectral bands. For example, visible and infrared light. You can deploy XAI-trained models to “split” high-resolution images into smaller patches.
In the domain of medical images, XAI models are used to detect chest pneumonia through X-rays. Similarly, image recognition is another use case of Explainable AI in the area of visual images. Using Visual AI, you can train custom AI models to recognize images or objects (contained in captured images).
XAI models and algorithms are effective based on their degree of accuracy or interpretation. Statistical relationship models like linear regression, decision trees, and K-nearest neighborhoods are easy to interpret but less accurate. For neural network models to be interpretable and accurate, high-quality data must be fed into the AI model.
XAI has tremendous potential in the domain of data science. For example, Explainable AI is used in the statistical production systems of the European Central Bank (ECB). By linking user-centric desiderata to “typical” user roles, XAI can outline methods and techniques used to address each user’s needs.
Next, let’s discuss the common tools and frameworks used in Explainable AI.
In recent times, AI researchers have worked on multiple tools and frameworks for promoting Explainable AI. Here is a look at some of the popular ones:
What-If: Developed by the TensorFlow team, What-If is a visually interactive tool used to understand the output of TensorFlow AI models. With this tool, you can easily visualize datasets along with the performance of the deployed AI model.
LIME: Short for Local Interpretable Model-agnostic Explanation, the LIME tool has been developed by a research team at the University of Washington. LIME provides better visibility into “what’s happening” within the algorithm. Additionally, LIME offers a modular and extensible way to explain the predictions of any model.
AIX360: Developed by IBM, AI Explainability 360 (or AIX 360) is an open-source library used to explain and interpret datasets and machine learning models. Released as a Python package, AIX360 includes a complete set of algorithms that cover different explanations along with metrics.
SHAP: Short for Shapley Additive Explanations, SHAP is a game-based theoretical approach to explain the output of any machine learning model. By using Shapley values from game theory, SHAP can connect optimal credit allocations with local explanations. SHAP is easy to install using PyPI or Conda Forge.
Organizations must have a complete understanding of their AI-powered decision-making processes through AI monitoring. Explainable AI enables organizations to easily explain their deployed ML algorithms and deep neural networks. Effectively, it helps in building business trust along with the productive use of AI and ML technologies.