Description: An explainer is a tool or method used to provide explanations for the predictions of artificial intelligence (AI) models. Its main goal is to make AI models more transparent and understandable for users, allowing them to comprehend how and why certain decisions are made. In a context where AI is applied in critical areas such as medicine, justice, and finance, the ability to explain a model’s decisions becomes essential for building trust and facilitating adoption. Explainers can take various forms, from graphical visualizations showing the importance of different features in a prediction to textual descriptions detailing the decision-making process of the model. This interpretative capability not only helps users better understand the results but also allows developers to identify and correct biases or errors in the models, thereby improving their performance and fairness. In summary, explainers are fundamental to explainable artificial intelligence, as they promote transparency and accountability in the use of advanced technologies.
History: The concept of explainable artificial intelligence (XAI) began to gain attention in the 2010s when it became evident that AI models, especially those based on deep learning, were often ‘black boxes.’ 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 the decisions made by AI systems. Since then, there has been significant growth in the research and development of explainability techniques.
Uses: Explainers are used in various applications across multiple domains, such as in healthcare to support professionals in understanding AI model diagnostic recommendations, in the financial sector to justify credit decisions, and in justice systems to explain sentencing or parole decisions. They are also useful in AI model development, allowing researchers to identify biases and improve model accuracy.
Examples: A practical example of an explainer is LIME (Local Interpretable Model-agnostic Explanations), which provides local explanations for the predictions of any AI model. Another example is SHAP (SHapley Additive exPlanations), which uses concepts from game theory to assign importance to features in model decisions. Both methods are widely used in the machine learning community to make models more understandable.
