What is Explainable AI?

1. What is Explainable AI?
2. Why is Explainable AI important?
3. Methods and Techniques for Explaining AI Models
4. Applications of XAI
5. 5 Softwares with AI Power

Thanks to OpenAI ChatGPT, neuroflash and Co., many people are now familiar with AI. Just hit the prompt quickly on the keys and you'll find the result on the screen a few seconds later. But why does the result look the way it does? Why does artificial intelligence make the decisions it does and not otherwise? This is where Explainable AI (or XAI) comes in: This concept aims to make the decision-making process of AI intelligible. In this article, you will find out exactly what this means, why it is important at all and what methodological approaches there are.

What is Explainable AI?

Imagine you work for a company that uses artificial intelligence to make complex decisions. But how can you ensure that the decisions made by the AI are understandable and based on trustworthy data? Here, Explainable AI (XAI) comes into play. XAI refers to methods and techniques with which AI models can be made understandable for humans. This means that the algorithms must be explainable and should provide transparent models.

An example is a model where you can see which data were used and how they were interpreted. The explainability of AI models is especially important for companies and organizations that are required to disclose their decision-making processes due to regulatory requirements or ethical considerations. With XAI, people can better understand why certain decisions are made and what impact they could have on their lives. Overall, XAI allows people to have more control over AI systems and thus develop more trust in their use.

 

Why is Explainable AI important?

The explainability of artificial intelligence (AI) is crucial for companies and people. It's about understanding the decisions of AI models and being able to trust them. If a company, for example, uses an AI model to predict sales, they need to be able to explain how the model made its decisions. This also means that it is necessary to understand the data on which the model was trained. Without explainability, it can be difficult to detect inappropriate or discriminatory decisions of the models and to act accordingly. Additionally, higher explainability can contribute to building more trust in AI models and increasing their acceptance among users. Thus, Explainable AI (XAI) is not just a trend or an option - but a necessity for businesses and society as a whole. Other important aspects here are:

• Trust and Acceptance: People are more likely to trust and adopt technologies that they can understand. If users can understand how an AI system arrived at a decision, they are more likely to accept these decisions and react to them, especially in critical areas such as medicine, finance and law.

• Error correction: The traceability of decision-making processes allows developers and users to identify and correct errors within AI models. This is particularly important as even small errors in the data or algorithm can lead to large deviations in the results.

• Accountability and Governance: In many applications, it is necessary for the decisions of the AI to be justified to stakeholders, such as customers, patients or even regulators. XAI allows responsibility to be clearly assigned and legal as well as ethical standards to be met.

• Avoidance of Bias: AI systems can unintentionally learn biases from their training data. XAI helps to recognize and minimize such bias to ensure fairer and more objective decisions.

• Security: In safety-critical systems, such as autonomous vehicles or surveillance systems, it is essential to understand how decisions are made in order to avoid and manage potential danger situations.

Methods and Techniques for Explaining AI Models

Layer-wise Relevance Propagation

To make the functioning of AI models transparent and comprehensible, research makes use of various XAI techniques. One of these techniques is Layer-wise Relevance Propagation (LRP), which serves to identify which features of input data contribute significantly to the results of a neural network. LRP works backwards, identifying relevant neurons and their connections to clarify which aspects of an input vector influenced the outcome.

Counterfactual Methods

Counterfactual methods, on the other hand, take an experimental approach. They change the input data afterwards - be it texts, images or diagrams - and observe how these changes affect the model's output. This makes it possible to identify which variations in the data lead to different decisions by the AI.

LIME

Another important approach are locally interpretable, model-agnostic explanations, known as LIME. This method aims to explain the predictions of AI models regardless of their complexity by surrounding complex predictions with simplified models which are then easier to interpret.

GAM

The generalized additive model (GAM) is also a technique used to understand the relationship between input variables and the prediction. GAMs are useful because they allow you to isolate the influence of individual variables on the outcome while modeling the relationships between them.

Rationalization

Finally, there is rationalization, which is particularly used in AI-based robots. This technique enables machines to explain their actions in human language, making their decision-making process understandable.

Applications of XAI

Explainable AI (XAI) has a wide range of use cases, each bringing specific challenges and benefits:

High Frequency Trading (Algorithmic Trading): In high-frequency trading, algorithms are used to trade large volumes of securities in very short time intervals. XAI can be used here to make trading decisions transparent and understand why an algorithm gives certain buy or sell recommendations. This not only increases the trust of traders and regulators, but can also help understand complex market phenomena and ensure regulatory compliance.

Medical Diagnostics: In medical diagnostics, XAI can contribute to explaining diagnostic decisions based on AI-based analysis of medical images or patient data. Understanding the reasons for decisions is particularly critical here as it promotes the acceptance of AI support among doctors and patients and can help reduce misdiagnosis and improve individual treatment plans.

Self-driving vehicles: With autonomous vehicles, it is essential to understand how the vehicle makes its decisions, such as why it chooses a certain route or how it responds to unexpected obstacles. XAI can increase safety in this area by providing insight into the decision-making processes of the vehicle AI, thus boosting user and public trust in this technology.

Neural Network Imaging: In image processing, neural networks are used to classify images, recognize objects and interpret patterns. XAI can help decipher the often complex inner workings of these networks. This is particularly useful in areas such as satellite image analysis or clinical imaging, where it is important to understand the underlying patterns that lead to a particular classification or recognition.

Military Purposes: In the military sector, AI is used for simulating and training combat strategies. XAI can contribute to making the decisions of the AI transparent, thus improving the understanding and effectiveness of training programs. This allows strategies to be better evaluated and optimized, leading to more efficient preparation for real operations.

5 Softwares with AI Power

You want to try out the possibilities of AI yourself? Then take a look at our categories for AI text generators and AI image generators on OMR Reviews. There you will find numerous providers, get an overview of all the functions of the tools and can find the right tool for your business based on verified user experiences. We have brought you five exciting tools with AI power:

neuroflash

OpenAI ChatGPT

Creaitor

Retresco Textengine

Conversionmaker.ai