As enterprises expand their artificial intelligence (AI) efforts, they must address important and often difficult questions. Is AI being used responsibly? Can the results that AI produces be explained? Because data underpins all AI processes, this series of blog posts looks at important AI questions from the standpoint of data, data management, and data governance. This second post focuses on explainable AI. The final post in the series will examine federated learning.
The first post in this series discussed four principles of responsible and ethical AI: fairness, privacy, security, and interpretability (aka explainability). AI models are now embedded in all aspects of our lives, affecting important decisions from who gets hired to who gets approved for a loan. Explainable artificial intelligence (XAI) has become crucial for understanding how an AI model reaches decisions and for identifying sources of error.
This post examines why explainable AI is important, the associated challenges, and the crucial role that data plays.
First, it’s important to understand what XAI is and why it’s needed. AI algorithms often operate as “black boxes” that take input and provide output with no way to understand their inner workings. The goal of XAI is to make the rationale behind the output of an algorithm understandable by humans.
For example, many AI algorithms use deep learning, in which algorithms learn to identify patterns based on mountains of training data. Deep learning is a neural network approach that mimics the way our own brains are wired. Just as with human thought processes, it can be difficult or impossible to determine how a deep learning algorithm arrived at a prediction or decision.
Decisions about hiring and financial services use case such as credit scores and loan approvals are important and worth explaining. However, no one is likely to be physically harmed (at least not right away) if one of those algorithms makes a bad recommendation. But there are many examples where the consequences are much more dire.
Deep learning algorithms are increasingly important in healthcare use cases such as cancer screening, where it’s important for doctors to understand the basis for an algorithm’s diagnosis. A false negative could mean that a patient doesn’t receive life-saving treatment. A false positive, on the other hand, might result in a patient receiving expensive and invasive treatment when it’s not necessary. A level of explainability is essential for radiologists and oncologists seeking to take full advantage the growing benefits of AI.
To expand on the idea of what constitutes XAI, the National Institute of Standards (NIST), part of the U.S. Department of Commerce, defines four principles of explainable artificial intelligence:
Here are examples of how these principles apply.
NIST defines five types of explanation:
The principle of meaningfulness is satisfied when a user understands the explanation provided. For a given AI algorithm, there may be different types of users who require explanations. In the self-driving car example, an explanation that satisfies the driver of the car, like “the AI categorized the plastic bag in the road as a rock, and therefore took action to avoid hitting it” would not satisfy the needs of an AI developer attempting to correct the problem. The developer needs to understand why the plastic bag was misclassified.
Explanation accuracy is separate from output accuracy. An AI algorithm needs to accurately explain how it reached its output. If a loan approval algorithm explains a decision based on an applicant’s income and debt when the decision was actually based on the applicant’s zip code, the explanation is not accurate.
An AI system can reach its knowledge limits in two ways. The input could be outside the expertise of the system. NIST uses the example of a system built to classify bird species. If you give it a picture of an apple, the system should explain that the input is not a bird. Alternatively, if you give the system a blurry picture, it should report that it cannot identify the bird in the image, or that its identification has very low confidence.
These principles help define the output expected from XAI, but they don’t offer any guidance on how to reach that output. It can be useful to subdivide XAI into three categories:
For neural networks in particular, explainable data is the only category that is straightforward to achieve—at least in principle. Much ongoing research is focused on how to achieve explainable predictions and algorithms. There are two current approaches to explainability:
Explainable models are sometimes referred to as “white box” models. As noted in a recent blog, “with explainable white box AI, users can understand the rationale behind its decisions, making it increasingly popular in business settings. These models are not as technically impressive as black box algorithms.” Explainable techniques include decision trees, Bayesian networks, sparse linear models, and others.
Researchers are also looking for ways to make black box models more explainable, for instance by incorporating knowledge graphs and other graph-related techniques.
Explainable data is the most attainable category of XAI. However, given the mountains of data that may be used to train an AI algorithm, “attainable” is not as easy as it sounds. The GPT-3 natural language algorithm is an extreme example. Although the model is capable of mimicking human language, it also internalized a lot of toxic content from the internet during training.
As Google notes, an “AI system is best understood by the underlying training data and training process, as well as the resulting AI model.” This understanding requires the ability to map a trained AI model to the exact dataset that was used to train it, with the ability to examine that data closely, even if it’s been years since a version of a model was trained.
One of the easiest ways to enhance the explainability of a model is to pay close attention to the data used to train it. During the design phase, teams have to determine where the data to train an algorithm will come from, whether or not that data—assuming that it exists—was obtained legally and ethically, whether the data contains bias, and what can be done to mitigate that bias. This is a big job that shouldn’t be underestimated; 67% of companies draw from more than 20 data sources for their AI.
It’s also important to carefully exclude data that is irrelevant or should be irrelevant to the outcome. Earlier, I mentioned the possibility that a loan approval algorithm could base decisions in large part on an applicant’s zip code. The best way to ensure that an algorithm’s output isn’t based on a factor that should be irrelevant—like a zip code that often serves as a proxy for race—is not to include that data in the training set or the input data.
Because explainable data is essential to XAI, your organization needs to cultivate best practices for data management and data governance. These best practices include complete traceability for the datasets used to train each version of each AI model you operate.
At NetApp, we specialize in helping companies get more from their data. We help you manage data everywhere—on premises and in the cloud. It’s how we make data accessible, protected, and cost optimized.
NetApp® AI experts can work with you to build a data fabric—a unified data management environment spanning across edge devices, data centers, and public clouds—so your AI data can be efficiently ingested, collected, stored, and protected.
NetApp AI solutions give you the tools you need to expand your AI efforts.
Adopting the NetApp AI Control Plane and Data Ops Toolkit can enable your team to manage data efficiently and securely, while ensuring the traceability and reproducibility that are an essential foundation of explainable data.
To find out how NetApp can help you deliver the data management and data governance that are crucial to explainable AI, visit netapp.com/ai.
Mike McNamara is a senior leader of product and solution marketing at NetApp with 25 years of data management and data storage marketing experience. Before joining NetApp over 10 years ago, Mike worked at Adaptec, EMC and HP. Mike was a key team leader driving the launch of the industry’s first cloud-connected AI/ML solution (NetApp), unified scale-out and hybrid cloud storage system and software (NetApp), iSCSI and SAS storage system and software (Adaptec), and Fibre Channel storage system (EMC CLARiiON). In addition to his past role as marketing chairperson for the Fibre Channel Industry Association, he is a member of the Ethernet Technology Summit Conference Advisory Board, a member of the Ethernet Alliance, a regular contributor to industry journals, and a frequent speaker at events. Mike also published a book through FriesenPress titled "Scale-Out Storage - The Next Frontier in Enterprise Data Management", and was listed as a top 50 B2B product marketer to watch by Kapos.
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