Explainable AI examples help businesses understand the way AI systems execute tasks. Moreover, Explainable AI examples appear in various industries including healthcare, manufacturing, finance, automobiles, etc.
AI systems certainly possess an immense potential that builds trust and enhances capabilities to easily adopt the technology. Further, explainable AI and its examples help understand the process in which machines provide outcomes. As a result, this helps experts to develop more transparent AI models.
According to a definition by Wikipedia, “Explainable AI refers to methods and techniques in the application of artificial intelligence technology such that the results of the solution can be understood by humans. ”
AI is often used to resolve real-world problems. Further, it gathers data, executes certain operations, and processes the data for actionable information.
Hence, in this article, we will understand the concept of Explainable AI, its examples.
Understanding Explainable AI and its Examples
Firstly, determining the way deep learning and machine learning models work is a tedious task. Moreover, AI models and algorithms that use deep learning and neural networks are not easily comprehensible.
According to Keith Collins, Executive Vice President and CIO at SAS, “The term ‘explainable AI’ or ‘interpretable AI’ refers to humans being able to easily comprehend through dynamically generated graphs or textual descriptions the path artificial intelligence technology took to make a decision.”
AI models are also responsible for operating pivotal processes, for instance in autonomous vehicles. Hence, users often require explanations and information about the way models come to their conclusions.
As a result, Explainable AI comes into play. Explainable AI or XAI is a suite of processes that help develop, comprehend, and interpret outcomes. It also addresses the way AI systems are created.
XAI also refers to the method and techniques that execute processes in AI technology. Moreover, it makes it easier for users and developers to comprehend the outcome and the solutions.
In other words, XAI helps explain the approach with which AI systems work. Therefore, it monitors various approaches like:
- Models that are naturally explainable. That is to say, these models are easy, simple, and transparent to understand.
- Models that are black-box in nature. These models require deep explanation using different and replicating models that imitate the behavior of an original model.
XAI principles are a series of regulations and guidelines that raise the fundamental aspects that should be adopted. Therefore, in order to understand Explainable AI, it is also important to learn about its principles. Hence, here are the four major principles in XAI:
- Firstly, AI systems must provide explanations that provide evidence.
- Further, explanations should be easily comprehensible for the users of the AI systems.
- Moreover, explanations must be accurate in interpreting the processes in AI systems.
- AI systems must also function within set limits.
A survey by PWC states that 82% of CEOs need to understand AI-based decisions. Further, AI is an important aspect of the modern world. Hence, users become more curious and need to understand the “how”. Moreover Explainable AI is a vast and multidisciplinary concept that helps evolve various technological phenomena.
AI methods generally comply with the categories Machine Learning (MR), Machine Reasoning (MR), and integration of ML and MR. Further, with the introduction of XAI, interpretability, and explainability are incorporated at various levels of complex AI methods. Moreover, XAI provides explanations for various AI methods and the environment. The concept of explainability also incorporates components like data, predictions, and algorithms.
That is to say, the primary components in XAI are explanations, explainability of data, explainability of ML, and explainability of MR. Moreover, XAI implements explainability and interpretation between ML and MR to create explanations.
However, there are multiple ways in which an AI model operates and generates actionable insights. Hence, here is an overview of how XAI works in the AI development process:
- Pre-modelling Explainability: It is the first stage in XAI that comprehends data and develops models. Moreover, it uses the following methodologies:
- Exploratory Data Analysis
- Dataset description, standardization, and summarization.
- Explainable components in engineering.
- Explainable Modeling: This stage refers to the development of easily explainable models. Further, it includes the following methodologies:
- Adoption of Explainable Models.
- Hybrid Models.
- Common Prediction and Explanation aspects.
- Architectural Adaptations and Regularizations.
- Post-modelling Explainability: It is a stage that refers to pre-developed models that employ extract explanations. IT also uses the following methodologies:
- Apprehensive Mechanisms.
- Backward Propagation.
- Proxy models.
- Activation maximization.
- XAI is preferable as it offers accountability to its users. For instance, XAI helps develop autonomous vehicles that can explain and describe their reasons for certain decisions in case of an accident.
- It is also integral in situations that require analytical and transparent decisions. Especially in industries like healthcare where there is confidential and sensitive information.
- Moreover, it builds and develops reliable interactions between humans and machines.
- Most importantly, it offers a higher level of visibility in the process of model decision-making.
- Firstly, XAI is a comparatively new technological concept in the areas of research. Hence, there are still challenges that require addressing. The first challenge that occurs is the expense of model performance accuracy. Since XAI systems are still developing they have a lower performance.
- XAI models are also more challenging to train due to complex neural networks and algorithms. Further, comprehending these components and their functions is intricate in nature.
- Moreover, as they are complex and challenging to train, XAI systems may also face issues during deployment.
Explainable AI Examples in Healthcare:
Firstly, Explainable AI can help explain the process of diagnosis while diagnosing a patient with diseases. Further, it can help healthcare professionals describe the diagnosis and the technique of treatments for patients. Hence, it will build trust between patients and their healthcare providers. Moreover, XAI can also contribute by interpreting medical imaging data that can diagnose terminal diseases.
Examples of Explainable AI in Manufacturing:
Manufacturing processes often depend on assembly lines that constantly require adjustments. Therefore, with XAI there is an improvement in machine-to-machine communication and comprehension. As a result, it can help generate more real-time awareness between humans and machines.
Explainable AI Examples in Defense:
Military training applications and software can use XAI to clarify and simplify decisions by an AI system. Hence, it helps reduce any potential threat that occurs due to ethical challenges. These threats often include misidentifying objects or missing targets.
Examples of Explainable AI in Autonomous Vehicles:
The automotive industry constantly looks for solutions to improve the execution and implementation of actions by an autonomous vehicle. Here, XAI provides explainability methods for AI algorithms to make an accurate decision in case of a fatal event. Moreover, it focuses more on aspects that help in making safety-critical decisions. Further, it helps create real-time awareness in case of accidents or potential fatalities.
Explainable AI Examples in Identifying Fraudulent Activities:
XAI can also help the finance industry identify fraudulent activities. That is to say, it can help explain certain transactions that may be flagged as “suspicious” or “legitimate”. Hence, it can help eliminate potential challenges without an unfair bias or any discrimination issues.
Examples of Explainable AI in Marketing:
AI and Machine Learning continuously help evolve Marketing Operations. Moreover, the technologies offer solutions to optimize marketing strategies for better ROI. Marketers often question the rationale and reasoning that AI provides. Hence, with XAI marketers can monitor the process of decision-making and develop their trust with the insights. It also helps marketers identify and eliminate issues in their AI models for more accurate outcomes and insights.
Firstly, XAI would be the end of Black box AI, which refers to AI solutions that use machine learning for decision-making. Although, these solutions fail to provide the rationale behind the same. Hence, it will give rise to explainability and interpretability in the process of decision-making that uses ML and MR.
XAI also deals with the practical and ethical challenges around Black box AI. Therefore, businesses now focus on transparency and prefer ML and MR models. Businesses require platforms to justify the logic behind insights and prove their validity.
It is also important to understand that AI systems are developed by human beings. That is to say, there will be unconscious biases and agendas that train the AI algorithm. Hence, XAI helps mitigate unconscious or algorithmic biases in the system.
According to Mario Savvides, Bossa Nova Robotics Professor of Artificial Intelligence, Electrical and Computer Engineering and Director of the CyLab Biometrics Center at Carnegie Mellon University states, “Explainable AI systems can be architected in a way to minimize bias dependencies on different types of data, which is one of the leading issues when complete black box solutions introduce biases and make errors.”
Moreover, XAI can eliminate and reduce biases within data sets. Hence, it helps ensure that AI systems are ethical and practical while generating insights.
Explainable AI examples portray the way businesses can use comprehensive, transparent, interpretable, and reliable solutions. Further, XAI gains the trust of users and complies with guidelines, standards, and regulations.
Explainable AI examples also demonstrate how the new and developing approach helps industries in various situational decisions.