Have you ever been surprised by a conversational AI’s response and wondered why it answered your question that way? It turns out the next AI trend seeks to answer this very question.
Companies (including Google and IBM) are starting to provide more visibility into how their AI models work, making it easier to identify potential biases, and ensure that these systems are being used ethically and responsibly. And they’re doing it with a collection of methods and techniques known as explainable AI.
What is Explainable AI?
Traditional machine learning models can be thought of as black boxes, where an engineer feeds in data, and the model spits out a result. But we don’t necessarily understand how it arrived at that decision. As AI models increase in their sophistication, it’s becoming increasingly important to understand how they make decisions.
In contrast, explainable AI, or XAI for short, refers to the ability of an AI system to provide a clear and understandable explanation of how it arrived at a particular decision or prediction.
XAI aims to make AI more transparent, allowing us to better understand how it works and why it made a particular decision. With visibility into the AI decision-making process, we can also identify potential biases and ensure that these systems are being used ethically and responsibly.
How Does Explainable AI Work?
There are several methods and techniques that engineers use to visualize and explain the decision-making process of AI systems. The specific explanation models engineers choose to use will depend on the AI model and its intended use case.
One explainable AI approach is to use visualization techniques to show how the AI system arrived at a particular decision. For example, in a medical diagnosis system, a visualization can show which parts of an image or scan were most important in arriving at the diagnosis.
One method of XAI is to use decision trees to illustrate how the AI system arrived at a particular decision. Decision trees are a visual representation of the decision-making process, where each node represents a decision, and each branch represents a possible outcome of that decision. By using decision trees, we can better understand how the AI system arrived at a particular decision and identify potential areas for improvement.
Natural Language Processing
Another method of XAI is to use natural language processing techniques to create a textual explanation of the AI system’s decision-making process. This can be particularly useful in cases where a more detailed explanation is required. For example, in a medical diagnosis system, a textual explanation can be used to explain why a particular diagnosis was made based on the patient’s medical history and symptoms.
XAI can also be achieved through the use of model-agnostic methods, where the focus is on explaining the decision-making process of the AI system, rather than the specific details of the model. Model-agnostic methods can be used to explain the decisions made by any type of AI system, including deep learning models, decision trees, and support vector machines.
Why is Explainable AI Important?
As artificial intelligence becomes more widespread, the decisions AI models make will have even higher stakes. To safely integrate AI models into our lives, we’ll need to understand exactly how and why they make decisions. As a result, explainable AI offers several key benefits.
First, explainable AI ensures that the decisions made by AI systems are transparent and can be understood by humans. This is particularly important in fields such as healthcare, finance, and autonomous vehicles, where the consequences of a wrong decision can be severe.
Second, XAI can identify biases in AI systems, which helps prevent discrimination against certain groups of people. Because AI models are trained by human inputs, they are naturally prone to bias. And AI bias has serious implications for the people the models are biased against. Having clear visibility into an AI model’s decision making process can make it easier to address its biases.
Third, XAI can significantly increase the business value of AI systems by increasing productivity and mitigating regulatory and legal risk.
Lastly, XAI can improve trust in AI systems, which can lead to greater adoption and integration of AI into our lives.
Examples of Explainable AI
DARPA Explainable AI
One example of XAI is the Explainable Artificial Intelligence project by the United State’s Defense Advanced Research Projects Agency (DARPA), which is focused on developing new XAI techniques for use in military applications.
The goal of the XAI program is to create machine learning techniques that:
- Enable human users to understand, trust, and manage artificially intelligent partners.
- Produce more explainable models while maintaining a high level of learning performance.
FICO Credit Score Explanations
Another example is the AI system used by the credit rating agency FICO. FICO uses XAI techniques to provide clear explanations of why a particular credit score was assigned to an individual.
Google’s AutoML system uses XAI techniques to generate explanations of how it arrived at a particular machine learning model. This provides technical and non-technical professionals with added insight into the machine learning models they’re building.
Explainable AI is an important area of research that’s increasingly relevant as AI systems become more complex and sophisticated. By making AI more transparent, accountable, and trustworthy, XAI can help prevent discrimination, improve decision-making, and increase trust in AI systems.
As we continue to develop more advanced AI systems, it’s important that we also focus on making these systems more explainable so that we can understand how they work and ensure that they’re being used ethically and responsibly.