What Is Explainable Artificial Intelligence and Is It Needed?
Explainable Artificial Intelligence-XAI is a subject that has been frequently debated in recent years and is a subject of contradictions. Before discussing Artificial Intelligence (AI) reliability, if AI is trying to model our thinking and decision making, we should be able to explain how we really make our decisions! Is not it?
There is a machine learning transformation that has been going on sometimes faster and sometimes slower since the 1950s. In the recent past, the most studied and striking area is machine learning, which aims to model the decision system, behavior, and reactions.
The successful results obtained in the field of machine learning led to a rapid increase in the implementation of AI. Advance work promises to be autonomous systems capable of self-perception, learning, decision making, and movement.
Especially after the 1990s, the concept of deep learning is based on the past, but the recursive neural networks, convolutional neural networks, reinforcement learning, and contentious networks are remarkably successful. Although successful results are obtained, it is inadequate to explain or explain the decisions and actions of these systems to human users.
The deep learning models designed with hundreds of layered millions of artificial neural networks are not infallible. They can lose their credibility quickly, especially when they are simply misled as in the case of a one-pixel-attack! Then it becomes inevitable to ask the question of how successful or unsuccessful!
The Department of Defense (DoD) states that the smarter, autonomous and symbiotic systems are facing challenges.
“Explainable AI—especially explainable machine learning—will be essential if future warfighters are to understand, appropriately trust, and effectively manage an emerging generation of artificially intelligent machine partners.”
The complexity of this type of advanced applications increases with the successes and the understanding-explainability becomes difficult. Even in some conferences, there are only sessions where this topic is discussed.
It is aimed to explain the reasons for new machine/deep learning systems, to determine their strengths and weaknesses and to understand how to behave in the future. The strategy to achieve this goal is to develop new or modified artificial learning techniques that will produce more definable models.
These models are intended to be combined with state-of-the-art human-computer interactive interface techniques, which can convert models into understandable and useful explanation dialogs for the end user.
With three basic expectations, it is desired to approach the system: ▪. Explain the purpose behind how the parties who design and use the system are affected. ▪. Explain how data sources and results are used. ▪. Explain how inputs from an AI model lead to outputs.
“XAI is one of a handful of current DARPA programs expected to enable -the third-wave AI systems- where machines understand the context and environment in which they operate, and over time build underlying explanatory models that allow them to characterize real-world phenomena.”
If we set out from medical practice, after examining the patient data, both the physician should understand and explain to the patient that he proposed to the concerned patient the risk of a heart attack on the recommendation of the decision support system.
At this stage, firstly, which data is evaluated is another important criterion.
