Description: The explainability of artificial intelligence (AI) refers to the degree to which the decision-making process of an AI system can be understood by humans. This concept is fundamental in the field of AI ethics, as it implies that users and stakeholders must be able to comprehend how and why an AI system arrives at certain conclusions or recommendations. A lack of explainability can lead to distrust in technology, especially in critical applications such as healthcare, criminal justice, and finance, where decisions can have a significant impact on people’s lives. Explainability not only focuses on the transparency of algorithms but also on the ability of systems to communicate their processes in a way that is accessible and understandable to users. This includes presenting information clearly and allowing for questioning and verification of the decisions made by AI. In a world where AI is increasingly integrated into decision-making, explainability becomes an essential pillar to ensure accountability, fairness, and trust in these technologies.
History: The concept of explainability in AI began to gain attention in the 2010s as machine learning systems became more complex and opaque. In 2016, the European Commission published a document addressing the need for transparency in AI systems, marking a milestone in the discussion of AI ethics. Since then, various methodologies and tools have been developed to enhance the explainability of AI models, especially in critical areas.
Uses: AI explainability is used in various applications, such as in healthcare to interpret diagnoses generated by algorithms, in the financial sector to justify credit decisions, and in the legal field to understand sentencing recommendations. It is also crucial in developing responsible AI systems and in regulating emerging technologies.
Examples: An example of AI explainability is the use of ‘decision trees’, which allow users to see how decisions are made based on specific features. Another case is the use of ‘LIME’ (Local Interpretable Model-agnostic Explanations) techniques that help break down the decisions of complex models like neural networks, providing understandable explanations for users.
