Selitettävyysskenaarioita

Selitettävyysskenaarioita, often translated as explainability scenarios, refers to hypothetical situations or use cases designed to illustrate how a particular explanation or explanation method would function in practice. These scenarios are crucial in the field of artificial intelligence and machine learning, particularly in the development of explainable AI (XAI). They help researchers, developers, and end-users understand the potential benefits and limitations of different explanation techniques by grounding them in concrete examples.

The purpose of a selitettävyysskenaario is to make abstract concepts of explainability tangible. For instance, a scenario might describe a doctor trying to understand why an AI system recommended a specific treatment for a patient. The scenario would then detail how different XAI methods could provide the doctor with relevant information, such as highlighting the patient's symptoms that most influenced the AI's decision or showing similar past cases. This allows for an evaluation of which explanation method is most effective and trustworthy in that specific context.

Selitettävyysskenaarioita can be tailored to various domains, including healthcare, finance, autonomous driving, and criminal justice. Each scenario typically outlines the user, the AI system's task, the context of use, and the specific explanation needed. By developing and analyzing these scenarios, stakeholders can better assess the practical implications of XAI, identify potential biases or errors in AI systems, and build greater trust in AI-driven decision-making. They are a vital tool for bridging the gap between theoretical XAI research and its real-world application.