Description: Self-explanation is an approach within explainable artificial intelligence that allows AI models to provide explanations for their predictions in a way that is understandable to users. This method seeks to demystify the internal workings of algorithms, facilitating the interpretation of their decisions. Self-explanation is based on the premise that users should understand not only the outcome of a prediction but also the process that led to that conclusion. This is especially relevant in contexts where automated decisions can have significant impacts, such as in medicine, justice, or finance. By offering clear and accessible explanations, user trust in technology is fostered, decision-making is improved, and greater collaboration between humans and machines is enabled. The main characteristics of self-explanation include transparency, interpretability, and accessibility, making it an essential component in the development of responsible and ethical AI systems. In a world where AI is increasingly present, the ability of models to effectively communicate with users is crucial for their acceptance and proper use.
History: Self-explanation as a concept has evolved over the years, especially with the rise of artificial intelligence in the 2010s. As machine learning models became more complex, the need to understand their decisions emerged. Research in the field of explainable AI began to formalize, highlighting the importance of transparency in AI systems. In 2016, the term ‘explainable AI’ gained popularity, driving the development of methods that allowed models to provide understandable explanations. Since then, self-explanation has been an active area of research, with significant advances in techniques and tools that enable AI models to communicate more effectively with users.
Uses: Self-explanation is used in various applications where understanding AI decisions is crucial. In the healthcare sector, for example, it is employed to explain diagnoses generated by AI systems, allowing doctors to understand the reasons behind a treatment recommendation. In the financial sector, it is used to justify credit decisions, helping lenders understand why a loan application was approved or rejected. Additionally, in the legal field, self-explanation can be fundamental for understanding decisions made by AI systems in judicial processes, ensuring transparency and fairness.
Examples: An example of self-explanation can be found in medical diagnostic systems, where an AI model can provide an explanation for why it suggests a specific diagnosis, citing symptoms and patient data. Another case is the use of credit models that explain the reasons behind the approval or rejection of a loan, such as the applicant’s credit history and income. In the justice field, some AI systems are designed to offer explanations for sentencing decisions, helping judges understand the reasoning behind the model’s recommendations.
