Description: Explainable Artificial Intelligence (XAI) is an emerging field within artificial intelligence that focuses on developing models and systems that are interpretable and understandable to humans. As AI integrates into various applications, from healthcare to business decision-making, the need to understand how and why these systems reach certain conclusions becomes crucial. XAI aims to address the ‘black box’ nature of many AI algorithms, where decisions are opaque and difficult to trace. This is not only important for user trust but also essential for complying with ethical and legal regulations. The ability to explain the decisions of an AI system can help identify biases, improve transparency, and facilitate accountability. In this context, XAI becomes a fundamental component to ensure that artificial intelligence operates fairly and responsibly, allowing users to understand and trust the automated decisions that affect their lives.
History: The term ‘Explainable Artificial Intelligence’ began to gain attention in the 2010s as deep learning models became more complex and their use expanded in critical sectors. In 2016, the U.S. Defense Advanced Research Projects Agency (DARPA) launched a research program on XAI, seeking to develop methods that would allow humans to understand and trust AI decisions. This push was driven by growing concerns about the opacity of algorithms and their impact on decision-making in areas such as criminal justice and healthcare.
Uses: XAI is used in various applications, including healthcare, where understanding AI diagnostic decisions is crucial. It is also applied in the financial sector for risk assessment and fraud detection, where explanations can help analysts validate automated decisions. In the legal field, XAI is used to ensure that predictive algorithms are transparent and fair.
Examples: An example of XAI is the use of decision tree models in credit risk assessment, where each decision can be easily explained and justified. Another case is the use of visualization techniques in neural networks to show how decisions are made in image recognition, allowing users to understand which features are relevant to the model.
