In Artificial Intelligence (AI) systems, particularly in the realm of Machine Learning, algorithms often provide accurate predictions but operate based on mechanisms that remain ambiguous even to developers. The lack of explanations associated with predictions represents a significant barrier to the adoption of these systems. “Explainable Artificial Intelligence” (XAI) refers to a set of AI technologies that offer an explanation for their predictions (post hoc explanations) and can therefore be intuitively understood by humans. This approach of “explainable” AI contrasts with the “black box” paradigm of classical Machine Learning systems, particularly Deep Learning algorithms such as neural networks.
The Importance of Explainability
The adoption of XAI technologies is becoming increasingly important and brings several advantages:
- Trust and Accountability: with increased transparency, the introduction of “explainability” enhances trust in the system and ensures a form of accountability. These two factors are crucial in applications such as self-driving cars, where decisions made by the AI system directly impact the user.
- GDPR Compliance: according to European Union guidelines, companies that use automated systems to process personal data should provide an explanation of how these processes make decisions (Article 15 h).
- Performance Improvement: greater insights into the decision-making processes of the algorithm help developers and data scientists improve AI models through more precise fine-tuning and allows for clearer identification of defects or vulnerabilities.
Explainable and Human-centric AI
XAI is part of the “Human-centric AI” paradigm of Industry 5.0, in which Machine Learning systems become complementary to human decisions. The explanations offered by an ML system should be intuitive and understandable even to non-experts. For example:
- In the biomedical field, the system should be able to both predict a pathology for a patient and explain to the doctor the factors that led to this prediction.
- In the financial sector, predictions about sales trends should be accompanied by an explanation: this helps connect the fields of Data Science with Business/Sales, increasing mutual trust between teams.
- In customer service, an XAI system should be able to assist customers and provide a clear explanation of why a particular issue was handled in a certain way.
- In marketing, a system can be built to personalize messages and advertisements to a user, also explaining the logic behind why a user is targeted for certain ads and not others. An XAI system could perform segmentation by dividing users into various groups based on their behaviors and characteristics, and accompanying each group with a clear explanation.
The use of XAI technologies enhances the effectiveness of the Human-AI duo because AI systems identify themselves as a support for human decision-making, rather than a substitute.
How to Implement an XAI System
The implementation of XAI methodologies can be done in various ways, ranging from the use of simpler and inherently “explainable” ML models to the use of supporting algorithms that accompany prediction models and provide an explanation for the decisions made by them. Some algorithms offer explanations related to individual predictions (“local explainability“) and specifically identify which elements were most significant in the decision-making process. Other algorithms, more complex, provide information about the general behavior of a model (“global explainability“), offering us an overview of the decision-making mechanisms on which the model is based.
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