Demystifying explainable AI: Understanding the basics

Since before, companies have employed machine learning (ML) practitioners to create artificial intelligence (AI) models to help frontline workers make safe decisions. 

A lot of businesses nowadays aim to create AI models that will transform their business and enhance worker productivity and safety.

However, anything can go wrong if AI models are too complicated to understand. More so, there might be trust issues between AI developers and the company and other problems at stake.

McKinsey’s survey about the state of AI in 2020 highlights explainability’s critical role in artificial intelligence. The study shows that while the example model was safe and accurate, the target consumers did not trust the AI system. It’s because consumers did not understand how AI made judgments. 

End-users have a right to understand the decision-making processes underpinning the technologies they are supposed to utilize. This is where explainable AI enters the picture.

Explainable AI (XAI) is a valuable tool for answering the how and why questions concerning AI systems. It can also be used to address emerging ethical and legal problems.

This article discusses explainable AI and its current state, benefits, and limitations.

What is explainable AI (XAI)?

Explainable AI (XAI) is a branch of artificial intelligence that focuses on creating AI systems that can explain their decision-making processes to people.

Understanding how AI systems make judgments is critical to guarantee that all business systems and processes are safe, dependable, and trustworthy in various domains.

Explainable AI systems produce interpretable explanations for their outputs, including the variables influencing the choice. Further, XAI is becoming increasingly important as AI systems are employed in high-risk sectors such as healthcare, banking, and transportation. 

How does explainable AI work?

Explainable AI systems function by building transparency and interpretability into AI models. XAI techniques include rule-based systems, decision trees, and model-independent methods.

The goal of the XAI project is to develop a set of ML methods that can:

• Make AI models that are easier to explain and still perform well in learning (prediction accuracy)
• Provide users with the tools they need to learn about and work with the next generation of AI partners

Model-agnostic approaches employ post hoc techniques to explainable AI models, such as Local Interpretable Model-Agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP).

Together, these models and advanced human-computer interaction (HCI) methods will provide users with explanation dialogues that are both understandable and helpful. 

3 significant benefits of explainable AI

Here are three significant advantages of investing in explainable AI:

Increased trust in AI systems

One of the biggest challenges with AI systems is their lack of transparency in decision-making. This results in mistrust, particularly in high-operating industries like healthcare and banking. 

Explainable AI addresses this worry by offering interpretable explanations of its outputs, which can assist consumers in understanding and trusting the decision-making process.

Examples:

• In healthcare – Explainable AI can point out the variables that led to a particular diagnosis or treatment prescription — helping clinicians to make smart decisions. 
• In finance – XAI can provide openness and accountability by detailing why a specific loan application was granted or refused.

Moreover, many businesses are cautious about implementing AI systems owing to worries about their dependability and transparency. Explainable AI may assist in overcoming these fears, making it easier for enterprises to embrace and reap the benefits of AI technology.

Improved decision-making

Traditional AI systems frequently make choices based on sophisticated, difficult-to-understand algorithms. 

Explainable AI, on the other hand, gives interpretable explanations of the decision-making process, allowing users to make smart choices. It also helps enhance decision-making by revealing the elements influencing specific choices. 

Examples: 

• In a loan approval system – Explainable AI can specify why a specific application was allowed or rejected. This can assist loan officers in making more informed judgments while reducing the potential for prejudice.
• In healthcare – XAI can explain why a particular therapy session was suggested, giving clinicians insight into the reasons that affected the choice. This can assist them in making better judgments and improving patient outcomes.

Enhanced accountability

Explainable AI alsos improve business accountability by providing a transparent audit trail of the decision-making process. XAI can explain the factors that influenced a particular decision — identifying and addressing bias or discrimination.

In a credit scoring system, for instance, explainable AI can show why a particular credit score was assigned to a customer. XAI also facilitates compliance with regulatory requirements in this area.

Limitations of explainable AI

While XAI has several benefits, it also has limitations that must be considered:

Complexity of AI models

Explaining the process of AI systems can be difficult due to the complexity of the underlying models.

For instance, deep learning models might include millions of parameters, which makes it hard to decipher their decision-making processes.

Need for specialized expertise

Expertise in both artificial intelligence and human-computer interaction is necessary to develop explainable AI systems. Because of this, creating XAI systems in-house might be difficult for many businesses.

Privacy and security concerns

There may be privacy and security risks associated with explainable AI systems. Going back to the medical field, XAI systems that explain patients’ diagnoses may disclose private information without the patient’s consent.

Explainable AI lets firms levearge the total value of deep learning

One of the most game-changing innovations of today’s generation is artificial intelligence. However, it is challenging to interpret how AI systems work. The emerging field of explainable AI aims to overcome this difficulty.

By offering explainability for the functionality and complexity of AI, explainable AI helps businesses reap the full benefits of artificial intelligence.

Furthermore, with the proper use of explainable AI, businesses can uncover hidden AI patterns and insights, enabling users to use the full potential of deep learning.