The use of artificial intelligence (AI) applications, along with the benefits they offer, has been on the rise in recent years. Researchers have shown a growing interest in AI models, which are utilized in various applications such as autonomous vehicle systems, mobile phones, financial management, risk analysis, and health diagnosis.
Acceptance of AI
The increasing availability of data and advancements in hardware technology have led to significant progress in artificial intelligence (AI) research and the development of more advanced AI systems. AI is a groundbreaking technology that is revolutionizing various fields, including healthcare, defense, finance, and autonomous vehicles. Complex AI algorithms, particularly deep learning algorithms, have been created to achieve remarkable results in diverse fields.
However, the lack of transparency and comprehensibility behind the behavior of AI models has led to a demand for more insight into how these models make decisions. This is especially important in critical areas such as healthcare, cybersecurity, autonomous driving, customer support systems, and event software development. This need for transparency and understanding has given rise to the research field of Explainable Artificial Intelligence (XAI), aiming to provide more descriptive information about the decision-making processes and outputs of AI systems.
XAI aim and terminology
XAI, or eXplainable Artificial Intelligence, aims to make AI systems more reliable, understandable, and transparent. It seeks to address the issue of trust by making the decision-making processes of AI systems understandable to users, including non-technical users. By increasing transparency, reliability, and fairness, XAI contributes to the social acceptance and widespread use of AI-based systems.
XAI covers tools designed to help people understand how complex AI models and algorithms work and to share this knowledge with a broad audience. The concepts of “explainability” and “interpretability” have been introduced to express the ability of AI to explain how and why it makes certain decisions or predictions. While these concepts are often confused, they emphasize different aspects of understanding AI models, with their unique characteristics and areas of application.
The main difference between explainability and interpretability lies in the target audience. Explainability aims to make the models’ decisions understandable to everyone, while interpretability is more focused on technical experts, researchers, and developers. Both concepts are critical to understanding how models work and their decisions, contributing to the responsible advancement of rapidly evolving technology.
Representation techniques
The concept of explainability has gained significant traction, leading to the development of various eXplainable Artificial Intelligence (XAI) methods in the past decade. These methods aim to provide insights into the inner workings of AI models and make their outputs more interpretable. Some common XAI techniques include visual explanations, instance-based explanations, feature-based annotation, and internal explainable techniques.
Visual explanations help users understand the contribution of each input to the output, while instance-based explanations delve into the underlying data distribution using specific data samples. Feature-based annotation produces feature contribution vectors, showing the impact of each feature on a prediction. Internal explainable techniques allow for the extraction of the model’s internal mechanisms, providing understandable information to users.
Every method plays a role in building trust in artificial intelligence by enabling individuals to gain a comprehensive understanding of the nature of artificial intelligence, its functionalities, and its implications for their daily lives and professional endeavors.