Description: Analytical models are tools that use statistical methods to examine and break down data, allowing analysts to gain meaningful and informed insights. These models are essential in the field of explainable artificial intelligence, as they facilitate understanding of how decisions and predictions are generated from data. Unlike black-box models, which operate opaquely, analytical models offer greater transparency, enabling users to interpret results more effectively. Their structure is based on algorithms that may include regressions, decision trees, and cluster analysis, among others. These models not only help identify patterns and trends in large volumes of data but also allow for hypothesis validation and informed predictions. The ability to explain the reasoning behind decisions made by these models is crucial in sectors where trust and accountability are paramount, such as in healthcare, finance, and justice. In summary, analytical models are fundamental for transforming complex data into understandable and useful information, promoting more informed and responsible decision-making.
History: Analytical models have their roots in statistics and data analysis, dating back centuries. However, their significant evolution began in the 1960s with the rise of computing and the development of more sophisticated statistical algorithms. As technology advanced, especially with the advent of artificial intelligence in the 1980s and 1990s, analytical models became more complex and capable of handling large volumes of data. In the 2000s, the exponential growth of data and the need to interpret it effectively led to a greater focus on model explainability, giving rise to the category of explainable artificial intelligence.
Uses: Analytical models are used in a variety of fields, including medicine, where they help predict treatment outcomes; in finance, to assess risks and investment opportunities; and in marketing, to segment audiences and personalize campaigns. They are also applicable in scientific research, where they allow for theory validation and analysis of experimental data. In the business realm, they are used to optimize processes and enhance strategic decision-making.
Examples: An example of an analytical model is the use of linear regressions in market studies to predict the impact of price changes on sales. Another case is the use of decision trees in medical diagnosis, where symptoms are analyzed to determine the likelihood of different diseases. In the financial realm, credit risk models use statistical analysis to assess the creditworthiness of borrowers.
