Description: Human interpretability refers to the degree to which a human can understand the reasons behind decisions made by an artificial intelligence (AI) system. This concept is fundamental in the field of explainable AI, where the goal is for machine learning models to be not only accurate but also comprehensible to users. Interpretability implies that the results of a model are accessible and that decisions can be explained in a clear and logical manner. This is especially important in critical applications, such as healthcare, criminal justice, and finance, where automated decisions can significantly impact people’s lives. A lack of interpretability can lead to distrust in AI systems, as well as difficulty in identifying biases or errors in the models. Therefore, human interpretability not only enhances transparency but also promotes accountability and ethics in the use of artificial intelligence. In summary, human interpretability is an essential component to ensure that AI systems are used effectively and responsibly, allowing users to understand and trust the decisions generated by these systems.
History: The concept of interpretability in artificial intelligence began to gain attention in the 1990s when researchers started to recognize the importance of understanding how machine learning models work. However, it was from 2016, with the rise of deep learning, that the need for interpretability became critical as these models became more complex and less transparent. In 2017, the term ‘explainable AI’ became popular, driving research and development into techniques that allow humans to better understand the decisions made by AI systems.
Uses: Human interpretability is used in various fields, such as healthcare, where AI models assist in diagnosing diseases, and it is crucial for professionals to understand the reasons behind recommendations. It is also applied in the financial sector, where credit decisions must be explainable to avoid discrimination and biases. In the legal sector, interpretability is essential to ensure that automated decisions are fair and transparent.
Examples: An example of human interpretability can be seen in medical diagnostic systems, where an AI model can provide not only a diagnosis but also an explanation of the symptoms and data that led to that conclusion. Another case is the use of credit scoring models that explain to applicants why they were granted or denied a loan, detailing the factors that influenced the decision.
