A Business’s Guide to Explainable AI – Principles & Use Cases

AI has become a surprising reality recently because everyone’s raving about it. Everyone has either incorporated AI into their lives or are planning to do so. The same goes for businesses as they are optimizing their business processes with AI technologies.
Since the AI’s use is increasing, people are putting a question mark on transparency. This is because business processes now use advanced analytics to make decision-making quicker. As for transparency, it will help see how AI models make decisions. One of the technologies that these models use is explainable AI. 

Having said that, if you are new to this AI technology, we are sharing what explainable AI is, along with the principles and use cases. So, let’s check out the details.

What is Explainable AI?

This branch of AI is about developing and implementing AI systems. In particular, it’s associated with AI systems that offer explanations of decision-making processes. In addition, this system explains the output or results of Machine Learning algorithms. To illustrate, explainable AI shows how a tool or robot created a solution (yes, it’ll explain the problem-solving process).

According to IBM, it’s the methodology used for implementing AI methods in businesses with proper accountability, fairness, and explanations. Truth be told, AI application is challenging, and black-box models have a role to play. When humans are unable to understand the decision-making process, it leads to problems. In fact, it creates a distressing situation. As humans, it must be easy for us to understand why a certain decision was made by an AI application.
It’s vital because when there is no transparency, it results in low trust levels. In particular, when the users don’t know how the system works, they won’t use it or rely on it. Not to forget, if an AI app makes an unfair and biased decision that causes harm to someone, it can lead to legal consequences. 

Now that you understand the core problem, it’s where we will use explainable AI. It is meant to resolve this black-box issue by providing proper information about the working of AI models. That’s why a variety of methods will be leveraged. To begin with, the decision-making process will be visualized. Another option is to simplify the computations and calculations of AI without any errors or accuracy-related issues.

Principles of Explainable AI

When it comes to explainable AI, you’ve to understand that there are various principles. To be particular, NIST has outlined four explainable AI principles. These principles make the entire concept of this AI technology.

Explanation

First of all, as the name suggests, it provides clear explanation of the process. It also explains different kinds of actions. There are no “maybe” and “something along those lines” here. This is because the explanations are on point. There are proper explanations of how the data is handled, and decisions are agreed upon. In simpler words, it explains the entire process which is used to take a step/decision. 

For instance, when an AI tool is used for credit scoring, it must have the features to explain why a certain application was rejected or accepted. In addition, it should show how income level and credit history led to a specific decision. It highlights all the important details.

Meaningful 

One of the most important explainable AI principles is meaningful. That’s because the explanations offered by AI tools must be easily understandable. The explanation should be meaningful as well, even if they are beginners. It’s an important point because technical or complicated words cannot help us understand why any decision was made by the tools. Consequently, it will lead to trust issues as well as confusion.

In case you’ve watched Grey’s Anatomy, you would know about technobabble. If an AI tool is being used for diagnosing illnesses, the explanation should be easy to understand for patients as well as doctors. It must have the factors they considered to make the diagnosis. These factors can include obesity, blood sugar levels, and blood pressure. If such information isn’t on hand, the doctors won’t be able to trust the diagnosis. Similarly, they won’t be able to recommend medications that can be fatal for patients.

Accuracy

It’s clear that AI systems need to provide proper explanations. However, the accuracy is a major question. This is why many people wonder how accurate the explanations are.

For this purpose, methods like important ranking are used. This method shows the important variables of a decision-making process. Some other methods used include SHAP and LIME. Both these methods provide global as well as local explanations of a certain decision or behavior.

Knowledge Limits 

Even with AI, you must think about the restrictions or limitations. When you create or use an AI system, you must understand the limitations as well as non-confirmed aspects. The AI systems should work according to the primary purpose or goal. In addition, it should be able to reach the desired goals and have confidence in the results or output. 

For instance, if you are using an AI system to make predictions about stock market trends, the results must be articulated. The level of certainty and ambiguity in a model must be easy to interpret. For example, it will show the errors in estimates. As a result, people have a clear picture, which helps make right and well-informed decisions according to the outputs.

How Does it Work?

Explainable AI explains the data that’s used for training the AI models, along with the reason it was selected. It also explains the predictions and which factors were considered to make that prediction. Not to forget, it also explains the role of different algorithms used by the AI model.

When it comes to AI and machine learning, it’s about understanding the why behind a specific decision. For this reason, explainable AI is about gaining insights into the model and how it extracts the answers. It shows why an app behaved specifically when it made a prediction or gave a suggestion.

There are two different approaches to explaining how AI systems work, including post-hoc and self-interpretative explanations.

Self-Interpretable

These models are basically the explanations that can be understood and read by human beings. In simpler words, it’s an explanation, and the most common examples include regression, logistic regressions, and decision trees.

Post-Hoc 

These explanations state how different algorithms work by modelling the behavior. For this reason, different software solutions are used, and you can use them on different algorithms. The best thing about these tools is that they don’t understand how specific algorithms work. It can answer how different outputs are shared according to the inputs. 

The tools can use different formats for explanations. In most cases, the graphical formats are used (pictures and graphs). To be precise, there will be maps and graphs for data analysis. Other than graphical formats, written reports and speech are also used.

Types of Algorithms of Explainable AI 

Explainable AI uses a variety of algorithms to complete the work and explain something. Some of these include the following;

LIME

This is the form of post-hoc explanation, and it is known to make decisions. In addition, it questions different factors and creates an interpretable model. This model helps understand the decision and provides a proper explanation of the decisions

SHAP

It also explains specific predictions through computation. It uses mathematical calculations to explain how specific features and factors helped make a prediction. It wouldn’t be wrong to call it a visualization tool, which helps understand the output. As a result, the outputs will be easier to grasp for everyone, including people with beginner-level knowledge.

Morris Method

This is also known as Morris sensitivity analysis and analyzes every step. It takes the stage or per-step approach. This means that one input is interpreted in one go. It helps understand which inputs are worthy of more analysis.

CEM

It is meant to explain the classification models. For this purpose, it outlines the unwanted as well as preferred features of a model. In addition, it explains why a specific event occurred at a specific time instead of another event.

SBRL

It explains the predictions of the model by combining common patterns into a list. For this purpose, the Bayesian stat algorithm is used. The if-then rule is used where data sets are used for mining the antecedents. As a result, the rules and their specific order are explained properly.

The Use Cases of Explainable AI

People who are new to understanding this AI branch keep looking for explainable AI examples. However, reading the use cases is an interactive way of understanding the backstage work. In addition, it will help understand the purpose of this AI field. 

Management of Financial Processes without Bias

The finance industry is not an easy industry to work. That’s because there are never-ending regulations. For this reason, explainable AI can be used to ensure accountability. For example, AI helps weigh the insurance claims while sharing the credit scores. In addition, it helps investors improve their portfolios. 

However, if the AI algorithms are biased, the investors will struggle a lot. Also, it can have a negative influence on the organization. To be honest, there are specific risks when you use tools to make credit decisions. That’s because you don’t want to make a silly decision only because the tool was acting out. This is why standards & regulations are vital. 

Moreover, it’s important that every stakeholder understands the decisions. These stakeholders include fraud auditors, lending agents, and investors. It’s essential as they aren’t aware of the technicalities of AI systems. However, when they have an explanation, they can make better decisions.

Operating the Autonomous Vehicles

Whether you are working on the corporate side or research side; explanations are important. These vehicles depend on data to locate specific objects around them, including distance and relationships. As a result, the cars make a decision to drive safely. These decisions must be easy to understand for people sitting in the car. 

In addition to drivers, the insurance companies and other authorities must understand them as well. It is beneficial in case of serious arrests because of accidents. For instance, they get to understand why brakes were applied and why a vehicle turned right rather than left.

Detection of Health Issues

In the past few years, AI has become important for people working in the AI industry. That’s because it’s helping people diagnose diseases and offer preventative care. In addition, it streamlines the mundane admin tasks. Since this is a sensitive industry, the patients and doctors will be sure that algorithms are doing their job. 

To illustrate, hospitals can use this AI to detect cancer and devise a treatment plan. The tool will show the reason behind a specific treatment. As a result, the doctors will be able to understand the treatment and explain it to the patients.

Benefits of Explainable AI

By this time, we are sure you understand the benefits of using explainable AI tools. Some of these benefits include;

More Credibility & Trust

Irrespective of the industry you are working in, building credibility and trust is important. Explainable AI can ensure there is no mistrust by offering proper insights into the decisions. That’s because when you can explain why a specific decision was implemented, it helps build trust. This increases the credibility that you are working responsibly.

Better Personalization 

For people working in marketing, they know that personalization is important. With this AI technology, personalization will become more effective. For instance, you will be able to explain the product recommendations and content-related suggestions. Moreover, it helps explain why specific ads were targeted at someone. In addition, it will help fix the nitty-gritties of campaigns.

Collaboration between AI & Humans

There was a time when AI was considered a black box. However, people are now openly using explainable AI to make suggestions and strategies. It has created a synergy, which helps use AI without compromising on human creativity and emotions.

Minimizing the Bias

AI models have the tendency to extend bias in the data. As a result, it can lead to bias and inequity. Having said that, explainable AI will help identify the biases and rectify them. In fact, people will be able to adjust their strategies and decisions to ensure there is no bias. Also, it encourages inclusivity and fairness.

Compliance with Regulations

With time, data protection and privacy regulations are upgrading. The businesses are under pressure to conform the regulations. Having said that, XAI can be used to ensure compliance with regulations. That’s because it helps understand specific decisions.

Top Considerations

When it comes to using explainable AI, many beginners don’t get the right results. For this purpose, we recommend following these considerations because they help achieve the desired results.

  • Make sure you are focusing on debiasing and fairness. As a user, you need to monitor fairness in the systems and scan them for possible bias.
  • You’ve to analyze the AI model and recommend the most logical and reasonable results. If the tools don’t provide a suitable solution, you can set an alarm.
  • The other factor is to manage the model risk. You’ve to mitigate these risks by getting alerts whenever the app doesn’t work properly. Furthermore, one must understand the reasons behind these deviations. 
  • Another consideration is to manage the models and run them as an integrated system. For this purpose, you’ve to put all the processes and tools in one place and monitor them. 
  • Last but not least, the AI projects should be implemented across cloud platforms, including public and private. That’s because it helps build confidence.

Should Businesses Should It?

It’s a quick yes. That’s because mastering this AI technology can help increase productivity and build trust. So, let’s see how a business can gain leverage from it.

Higher Productivity 

It becomes easier to find errors and lagging points in the system. As a result, the teams can make changes quickly and upgrade the systems to prevent errors. This makes sure the teams aren’t wasting time manually finding errors and spending hours fixing them. Consequently, the productivity instantly boosts.

More Trust

When you’ve to build trust, you’ve to be open to explaining. Having said that, explainable AI provides explanations to the customers and regulators. Consequently, they will certain about your offerings. 

Increased Business Value

When you can explain how a specific system works, the teams can confirm if the goals are being met. In addition, it will help see if the processes were insufficient at some point. It helps ensure that the AI apps are offering ROI and value. Similarly, the customers will get a valuable experience as well.

Mitigation of Risk

This AI can spot ethical or other issues that can land the business in legal or public battles. For instance, businesses can use these explanations to state why a specific process happened. Moreover, it will help ensure that internal policies are according to the regulations and laws.

Are There Any Limitations?

Of course, there are some associated with the use of explainable AI. On top of everything, there is a lack of consensus on many processes and terms. For instance, the definition of the same terms is different in multiple books and papers (there is no uniformity). This is a huge limitation because it can influence how a specific process is explained.

Frequently Asked Questions

Can you share examples of explainable AI?

The healthcare industry is a go-to example. That’s because these AI systems can help with the diagnosis of patients and explain the diagnosis. In addition, it can recommend treatment plans and medications, Not to forget, it shares an explanation of how a medicine will help.

What is the meaning of explainability in AI?

It is also called interpretability. It is used by machine learning to explain the statements. It explains the statements in a way that’s understandable to the general public.

Are interpretable AI and explainable AI the same?

No, they aren’t the same. Interpretable AI is more about understanding the internal processing of models. On the other hand, explainable AI explains the decisions. This means that interpretable AI needs more details.

Explainable and symbolic AI – tell the differences?

Symbolic AI explains everything in the form of designs. On the other hand, explainable AI uses logic and text to explain everything in human language.

Why should we use explainable AI?

It is suitable for non-tech people because it helps them understand the predictions. In addition, it can help improve the performance of a specific model by understanding the behavior.

The Bottom Line

Explainable AI is a popular choice. It helps ensure transparency and understanding of the predictions and recommendations. It’s safe to say that it can help support regulations and laws. As a result, the customers feel more supported, leading to higher trust levels. So, in the near future, its use will increase even more because AI’s use will increase. However, 90% of people don’t have the technical expertise to understand the process. Having said that, explainable AI will be able to help make sense!

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