Explainable AI can be defined as:
A set of processes and methods to help human users understand and trust the results of machine learning algorithms.
As you can guess, this interpretability is very important. Because AI algorithms control many domains, this poses the risk of bias, faulty algorithms, and other problems. With transparency enabled by explainability, the world can truly harness the power of AI.
Explainable AI, as the name suggests, helps describe an AI model, its impact, and potential biases. It also plays a role in describing the accuracy, fairness, transparency, and outcomes of AI-driven decision-making processes of models.
Today’s AI-driven organizations should consistently employ explainable AI processes to help build trust and confidence in AI models in production. Explainable AI is also key to being a responsible business in today’s AI environment.
Because today’s AI systems are so advanced, humans typically perform a computational process to trace how an algorithm came to a result. This process becomes a “black box”, meaning it cannot be understood. When these unexplainable models are developed directly from the data, no one can understand what’s going on.
With explainable AI to understand how an AI system works, developers can ensure that the system works properly. It can also help ensure models meet regulatory standards and provide opportunities for model challenges or changes.
Difference Between AI and XAI
Some key differences help differentiate “regular” AI from explainable AI, but most importantly, XAI implements specific techniques and methodologies to help ensure that every decision in the ML process is traceable and interpretable. In contrast, conventional AI usually uses ML algorithms to get results, but it is impossible to fully understand how the algorithm got the results. In the case of conventional AI, it is difficult to check for accuracy, leading to a loss of control, accountability, and auditability.
The benefits of explainable AI
There are many benefits to any organization looking to adopt explainable AI, such as:
Faster results: Explainable AI enables organizations to systematically monitor and manage models to optimize business outcomes. Model performance can be continuously evaluated and improved, and model development fine-tuned.
Reduce risk: By employing explainable AI processes, you can ensure that AI models are explainable and transparent. Regulatory, compliance, risk, and other needs can be managed while minimizing the overhead of manual inspections. All of this also helps reduce the risk of accidental bias.
Build trust: Explainable AI helps build trust in production AI. AI models can be put into production quickly, interpretability is guaranteed, and the model evaluation process can be simplified and made more transparent.
Explainable AI Technology
There are some XAI techniques that all organizations should consider, and there are three main approaches: predictive accuracy, traceability, and decision understanding.
The first method, predictive accuracy, is the key to the successful use of AI in day-to-day operations. Simulations can be run and the XAI output compared to the results in the training dataset, which helps determine the accuracy of the predictions. One of the more popular techniques for accomplishing this is called Locally Explainable Models – Independent Explanation (LIME), a technique for interpreting classifier predictions through machine learning algorithms.
The second approach is traceability, which is achieved by restricting how decisions are made and establishing a narrower scope for machine learning rules and features. One of the most common traceability techniques is DeepLIFT, or Deep Learning Important Features. DeepLIFT compares each neuron’s activation to its reference neuron while demonstrating the traceable links between each activated neuron. It also shows dependencies between each other.
The third approach is decision understanding, which, unlike the first two approaches, is human-centred. Decision understanding includes educating organizations, especially teams working with AI, so they can understand how and why AI makes decisions. This approach is critical to building trust in the system.
Explainable AI Principles
To better understand XAI and its principles, the National Institute of Standards (NIST), part of the U.S. Department of Commerce, provides definitions of four principles that explain AI:
AI systems should provide evidence, support or reasoning for each output.
AI systems should give explanations that users can understand.
The explanation should accurately reflect the process the system uses to achieve its output.
AI systems should only operate under the conditions for which they were designed, and should not provide outputs when they lack sufficient confidence in the results.
These principles can be further organized as:
Meaningful: In order to implement the principle of meaning, users should understand the explanations provided. It also means that there may be multiple interpretations in the case of different types of users using AI algorithms. For example, in the case of self-driving cars, one explanation might be something like this… “The artificial intelligence classifies a plastic bag on the road as a stone and therefore takes action to avoid hitting it.” While this example applies to the driver, the Not very useful for AI developers looking to correct the problem. In this case, the developer must understand why misclassification occurs.
Interpretation accuracy: Unlike output accuracy, interpretation accuracy involves the AI algorithm explaining exactly how it arrived at the output. For example, if the loan approval algorithm interprets the decision based on the income applied for, when in fact it is based on the applicant’s residence, that interpretation will be inaccurate.
Knowledge constraints: The knowledge constraints of AI can be achieved in two ways, which involve input beyond the expertise of the system. For example, if building a system to classify bird species, given a picture of an “apple”, it should be able to explain that the input is not a bird. If the system is given a blurry picture, it should be able to report that it cannot recognize the bird in the image, or that it recognizes it with very low confidence.
The role of data in explainable AI
One of the most important components of explainable AI is data.
According to Google, regarding data and explainable AI, “an AI system is best understood through the underlying training data and training process, and the resulting AI model.” This understanding relies on mapping the trained AI model to the It depends on the ability to train on its precise data set, and the ability to closely examine the data.
To enhance the interpretability of the model, it is important to pay attention to the training data. Teams should determine the origin of the data used to train the algorithm, the legality and ethics of obtaining the data, any potential bias in the data, and what can be done to mitigate any bias.
Another key aspect of data and XAI is that data that is not relevant to the system should be excluded. For this to work, irrelevant data must not be included in the training set or input data.
Google recommends a set of practices for achieving explainability and accountability:
Program selection in pursuit of interpretability
Treat interpretability as a core part of user experience
Design interpretable models
Select metrics to reflect end goals and end tasks
Understanding Trained Models
Communicate explanations with model users
Extensive testing to ensure the AI system works as expected
By following these recommended practices, organizations can ensure explainable AI. This is key for any AI-driven organization in today’s environment.
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