Description: Process Transparency in the context of explainable AI refers to the clarity with which the processes and decisions of an artificial intelligence model can be understood. This concept implies that users, developers, and other stakeholders can access information about how a model makes decisions, what data it uses, and what criteria influence its outcomes. Transparency is fundamental to fostering trust in AI systems, as it allows users to comprehend and evaluate the logic behind automated decisions. Furthermore, process transparency helps identify potential biases and errors in models, which is crucial for ensuring fairness and ethics in the application of artificial intelligence. In a world where automated decisions can significantly impact people’s lives, the ability to break down and explain the internal workings of these systems becomes essential. Transparency not only refers to the accessibility of information but also to the models’ ability to provide understandable and meaningful explanations for their decisions, contributing to greater accountability and responsibility in the use of artificial intelligence.
History: Transparency in AI processes has gained attention since the early 2010s, when the use of complex algorithms, such as deep neural networks, began to proliferate. As these models became more sophisticated, concerns arose about their opacity and the difficulty in understanding how they arrived at their decisions. In 2016, the term ‘explainable AI’ began to be used in academic literature, driving research into methods to make AI models more interpretable and accessible. The growing concern over ethics in AI and the need for regulations have also contributed to the demand for greater transparency in automated decision-making processes.
Uses: Process transparency is used in various applications of artificial intelligence, including credit systems, medical diagnosis, and personnel selection. In the financial sector, for example, credit scoring models must be transparent so that applicants can understand the reasons behind the approval or rejection of a loan. In medicine, AI-assisted diagnostic systems must explain their recommendations so that healthcare professionals can trust their suggestions. Additionally, in hiring, personnel selection tools must be able to justify their decisions to avoid biases and discrimination.
Examples: An example of process transparency can be seen in the use of AI models in medical diagnosis, where visualization techniques are used to show healthcare professionals how a specific conclusion was reached. Another case is that of credit platforms that provide applicants with a breakdown of the factors that influenced their credit score. Additionally, some companies are implementing AI tools that offer natural language explanations for the decisions made, making it easier for non-technical users to understand.
