We know that the pure bandwidth of artificial intelligence is incredibly wide-reaching; from writing pieces of code to helping you perfectly word your next marketing campaign, the applications of AI in your daily life are practically too many to count. And as these uses become increasingly common and in the hands of everyday people, the importance of human interpretability in AI plays an even more crucial role.
As easy it would be to simply trust in AI tools and not focus on how they’re coming up with their outputs, any AI user knows that understanding how these tools work and who’s responsible in case of issues is absolutely essential.
In this article, we’ll dive into the human interpretability of AI and machine learning models, explaining exactly why transparency in AI is so important and how explainability in AI models is the next step forward for artificial intelligence.
Human Interpretability in Artificial Intelligence
Human interpretability in AI is easily explained: it refers to the extent with which an AI or machine learning system’s processes and decision making can be understood by humans and in human terms. Although it might seem easier to just let AI systems run and trust in their decisions, an increasing dependence on AI has led to some important questions about exactly how decisions are being made.
In short, we can break down the need for transparency in AI into two categories:
Explainable: this is quite straightforward–the way in which AI tools arrive at a decision must be understood and explained to users so that there is full transparency when it comes to decision making.
Justifiable: both the usage of AI tools and the way it arrives at a decision needs to be justified so that users know why AI tools are being used in this setting and how that might affect their outcomes.
In addition, ensuring that users and researchers alike understand AI models is key because:
Demystifying AI makes it more accessible: as more and more people put their faith in AI tools, how it works and makes decisions must be clear in order to ensure that all people can use it properly, not just those with high level understandings of tech.
Guaranteeing better decisions is better for all: the best decisions are backed by data and AI tools have the potential to completely transform how we make decisions–but only if the path to making these decisions is clear.
Improving AI tools can improve lives: we already know that the potential AI shows is vast and to continue to make the most of its potential, we need to guarantee transparency with how decisions are made.
Why is interpretability important in AI?
Many people think of AI systems as dark boxes with a somewhat unknown process happening, but are still willing to trust it with major tasks or decisions. And that’s because the majority of use cases for AI help make our lives easier, facilitating tasks that could be too time-consuming or data-heavy for humans to do on their own.
But as true it is that AI tools can take some responsibilities off our plate, we would be remiss to simply assume that AI systems can run on their own with little to no human understanding or interaction. Why? Let’s explore:
AI systems can be biased: as you’re well aware, AI systems are only as knowledgeable as the data they were trained on and the risk of bias is a real threat. For humans to trust outcomes suggested by AI systems and trust that they are bias-free and fair, their decision-making and training process must be clear—and understandable–to humans.
AI systems need to be adjusted to meet business needs: your business needs may change over time, meaning your AI model needs to be adjusted periodically to reflect your desired outcomes and goals. If you don’t know how your AI system is working, you’ll struggle to receive the optimal outcome.
AI systems can make mistakes: it might be easier to just believe that AI systems are infallible and incapable of being wrong, but that’s not the case and to mitigate risk, a clear understanding of how the model is working to correct any mistakes is key.
AI regulations are constantly changing: as the reach of artificial intelligence becomes increasingly widespread, local and international guidelines are evolving to ensure best practices; being able to change elements of your AI model to meet regulations is essential–and will continue to be so.
Methods to Better Understand Artificial Intelligence
As you can see, the need for transparency and interpretability with AI and machine learning models is paramount and to guarantee this, various methods have sprung up. For both users and scientists alike, the three methods outlined below provide increasing visibility as to what is going on behind the scenes with AI tools.
Explainable AI (XAI)
It’s in the name: explainable AI seeks to explain the science behind artificial intelligence tools, making their decision making process clear to users and those working to further develop them with the goal of achieving better control and accountability. AI professionals who prescribe to XAI seek to only use AI models once the actual workings and decision making processes of the tool is incredibly clear, using different techniques:
Prediction accuracy: knowing how much trust can be placed in an AI tool’s decision making process is absolutely essential and by comparing outputs with the AI’s training data, scientists can know exactly how accurate the AI’s suggestions are.
Traceability: to have more control over how AI tools arrive at decisions, scientists can limit the freedom AI systems have when making decisions, all the while providing more clarity on the specific decision making process that these tools boast.
Trust: all in all, scientists want to increase trust in AI tools so that their use is more widespread and helpful, but this requires a certain level of trust that only comes with full transparency and explainability.
Model extraction
Instead of diving directly into how specific AI models work, model extraction looks to use existing machine learning or AI models to explain how others work, therefore skipping the step of dissecting and understanding each individual aspect of the model. By trying to understand an AI model in context instead of as a standalone creation, scientists and users alike will benefit from a deeper comprehension of how the tool works across a wide range of uses, instead of just one.
Active learning
The last method for improving interpretability in artificial intelligence is one that seeks to view AI and machine learning models as growing and evolving projects; active learning trains models over time, constantly checking in on the accuracy of its outputs compared to previous situations and data sets to evaluate its overall effectiveness.
Through active learning, researchers can improve the model over time and based on their needs, ensuring that the model they’re using is up to date and understood.
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