The use of artificial intelligence (AI) has dramatically increased over time because increasingly more AI systems are becoming an integral part of our daily lives and high consequence environments like healthcare, finance, government, and management. As AI tools are improved to be more capable and reliable, users are increasingly relying on them not only for input but also transferring a portion of their own reasoning processes to the systems. This process of transferring parts of the human reasoning process to algorithmic systems is known as Decision Logic Transfer. Under this method, humans transfer a portion of their cognitive responsibilities to algorithm-driven systems, including: the ability to evaluate options, employ criteria, and decide on a course of action. Although there are advantages to Decision Logic Transfer, there are also some disadvantages to transferring cognitive responsibility from humans to AI systems. For example, AI systems can improve a company’s efficiency, provide consistency in all of that company’s decisions, process much larger volumes of data than humans can, and to some degree lessen the biases and errors that humans may make. Additionally, businesses are able to standardize their operating procedures, comply with regulations, and make scalable decisions as a result of this transfer. On the other hand, as people continue to rely more and more on AI, they will likely lose their ability to think critically, be aware of their surroundings, and take personal responsibility for their decisions. This continual reliance on AI may result in people simply being passive receivers of the algorithms’ output rather than active participants in the decision-making process. In high-stakes decisions, over-reliance, automation bias, and diffusion of responsibility may become significant problems.
Artificial Intelligence (AI) is used to develop decision-making systems (also known as intelligent automation), human knowledge transfer to machines, and the use of algorithms to arrive at decisions. There are many types of systems available for making decisions, including machine learning (ML), deep learning (DL), knowledge representation and expert systems, rule-based systems
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