Description: Explainability in artificial intelligence refers to the degree to which the actions and decisions of an AI system can be understood by humans. This concept is fundamental to ensuring transparency and trust in automated systems, especially in critical applications such as medicine, justice, and finance. Explainability allows users to comprehend how and why an AI model has reached a particular conclusion, which is essential for validating results and identifying biases. Furthermore, it fosters accountability, as developers and organizations can be held responsible for the decisions made by their systems. In a world where AI is increasingly integrated into everyday life, explainability becomes an indispensable requirement for social acceptance and ethical use of these technologies. A lack of explainability can lead to distrust and rejection of AI, highlighting the need to develop models that are not only accurate but also understandable to end users. In this regard, research in explainable artificial intelligence (XAI) aims to create methods and tools that facilitate the interpretation of models, allowing users to not only see the results but also understand the process behind them.
History: The concept of explainability in AI began to gain attention in the late 1990s and early 2000s when the need to understand complex AI systems was recognized. However, it was in the last decade, with the rise of deep learning models, that the lack of transparency became a critical issue. In 2016, the research community began to formalize the term ‘explainable artificial intelligence’ (XAI), driven by concerns about the use of AI in decisions that affect people’s lives. The U.S. Defense Advanced Research Projects Agency (DARPA) launched an XAI program in 2016, seeking to develop techniques that would make AI models more understandable.
Uses: Explainability is used in various AI applications, especially in sectors where automated decisions have a significant impact on people’s lives. In medicine, for example, AI models that assist in diagnoses must be explainable so that doctors can trust their recommendations. In the financial sector, institutions use credit models that must be transparent to comply with regulations and avoid biases. Additionally, explainability is crucial in criminal justice systems, where recidivism risk decisions must be understandable to ensure fairness.
Examples: An example of explainability in AI is the use of decision tree models, which allow users to clearly see how decisions are made based on input features. Another case is the use of visualization techniques in neural networks, such as Grad-CAM, which help researchers understand which parts of an image influence a model’s classification. In the financial sector, explanations of credit scoring models can help applicants understand why they were granted or denied a loan.
