In the past couple of years, artificial intelligence (AI) has made significant inroads into various industries including healthcare, manufacturing, and finance. According to a recent survey by Deloitte, more than 60% of executives believe that widespread adoption of AI will occur within the next three years. However, AI is not without its downsides and challenges. Because AI is a complex technology that learns from large amounts of data, it’s difficult for people to understand how it works. This means that AI systems need to be Explainable so that humans can understand why an action was taken or a decision was made. Unfortunately, most AI solutions aren’t very explainable yet. In this article, we’ll explore why AI needs to be explainable and how to make it so.
What is Explainable AI?
Explainable AI is the ability for humans to understand the decisions made by an AI system. This allows you to have a better idea of how the AI arrived at its conclusions. A good example is a decision tree. As a human, you can clearly understand how that tree arrived at a conclusion — even though the tree is a non-human algorithm. Explainable AI is particularly important for industries such as healthcare, where AI is being used to make critical decisions that affect human lives. For example, an autonomous car that is supposed to transport patients to the hospital should be programmed to avoid potential dangers on the road. It’s unlikely that this car would be programmed with a list of all possible dangers it could face on the road, so how can it “decide” how to avoid these dangers? It would need to use AI algorithms programmed to identify potential dangers and then take the appropriate course of action.
The Importance of Explainable AI
At present, most AI systems are black boxes — meaning that they’re designed in such a way that humans can’t understand their decision-making process. While this has great benefits for AI systems, it also means that humans can’t truly understand why an AI system made a particular decision. This presents some issues: As AI systems are integrated into industries like healthcare and law, where systems need to make critical decisions based on a patient’s health or a defendant’s criminal record, it’s crucial that humans can understand why an AI system made a particular decision. This is where explainable AI comes into play. By making AI systems more explainable, you ensure that humans have the necessary information to understand an AI system’s decision-making process.
Why is AI Currently Not Explainable?
One of the main reasons why AI systems aren’t currently very explainable is that they’re designed to learn from large amounts of data. By analyzing large amounts of data and making connections between various pieces of information, AI systems can learn to recognize patterns and eventually make decisions based on those patterns. Because AI is designed to learn complex patterns, it can be difficult for humans to understand how AI systems make decisions. This means that AI systems aren’t currently very explainable.
Ways to Make AI More Explainable
There are several ways you can make AI more explainable. These include
- Human-in-the-Loop: Human-in-the-Loop (HITL) is when a human is actively involved in the decision-making process of an AI system. A human might look at an AI system’s decision and then explain it to other humans.
- Explainable Models: One way to make AI more explainable is to build explainable AI models. Explainable models help humans understand how an AI system arrived at a particular decision by using a visual interface.
- Explainable Predictions: Another way to make AI more explainable is to create a model that predicts the probability of something going wrong. You can then use this model to explain why the probability is what it is.
- Open Source: Another great way to make AI more explainable is to make sure that your AI models are open source. This way, other people can check your algorithms and make sure that your models are legitimate.
- Visualization: You can also make AI more explainable by visualizing data so that humans can understand it more easily. This is helpful for both humans and AI to better understand data and make decisions based on it.
Popular Explainability tools for AI
There are a number of different tools that can be used to explain the decisions made by AI models. Some of the most popular explainability tools include:
- SHAP (SHapley Additive exPlanations) is a tool that can be used to explain the output of any machine learning model. SHAP uses game theory to estimate the importance of each feature in determining the model’s output.
- LIME (Local Interpretable Model-Agnostic Explanations) is another tool that can be used to explain the decisions made by machine learning models. LIME creates an “interpretable model” that is locally faithful to the original model, meaning that it can accurately explain the original model’s predictions for specific instances.
- ELI5 (Explain Like I’m 5) is a tool that can be used to generate simple explanations of machine learning models. ELI5 allows users to specify a particular instance and then generates an explanation of how the model arrived at its prediction for that instance.
- DeepLIFT (Deep Learning Important FeaTures) is a tool that can be used to explain the decisions made by deep learning models. DeepLIFT assigns importance scores to each neuron in a deep learning model, which can then be used to generate explanations of the model’s predictions.
- Layer-wise Relevance Propagation (LRP) is a tool that can be used to explain the decisions made by deep neural networks. LRP decomposes the output of a neural network into contributions from each neuron in the network, allowing for detailed explanations of the network’s predictions
Making AI more explainable is crucial for the adoption of AI. It is essential to help humans understand how AI systems work and make decisions. With the help of these tools, we can make AI more understandable to humans and thus reduce the concerns of potential misuse and abuse of AI systems.
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