Description: Automated Decision Making refers to the process of using algorithms and artificial intelligence models to make choices or recommendations without direct human intervention. This approach relies on analyzing large volumes of data, where systems can identify patterns and trends that may not be evident to humans. Automating decisions allows for greater speed and efficiency in decision-making, as well as reducing human biases. The applications of this technology span various areas, from optimizing business processes to personalizing user experiences. Automated Decision Making relies on machine learning techniques and predictive analytics, making it a powerful tool for companies looking to enhance their competitiveness and adaptability in a constantly changing environment.
History: Automated Decision Making has its roots in the development of artificial intelligence in the 1950s, when early algorithms began to be used to solve complex problems. Over the decades, the evolution of computing and the increase in data processing capacity have allowed these systems to become more sophisticated. In the 2000s, with the advent of Big Data and machine learning, Automated Decision Making began to be adopted in various sectors, including finance, e-commerce, and healthcare, where the need for quick and accurate decisions became critical.
Uses: Automated Decision Making is used in a variety of applications, including risk assessment in loans, personalization of recommendations on online platforms, supply chain optimization, and fraud detection. It is also applied in healthcare, where algorithms are used to diagnose diseases and recommend treatments. In marketing, it allows for audience segmentation and more effective targeting of advertising campaigns.
Examples: An example of Automated Decision Making is Netflix’s recommendation system, which uses algorithms to suggest movies and series based on the user’s viewing history. Another case is the use of artificial intelligence in fraud detection in banking transactions, where systems analyze behavioral patterns to identify suspicious activities. In healthcare, some platforms use algorithms to predict disease outbreaks and optimize the allocation of medical resources.
