Explainability refers to the understanding and interpretation of how an AI model makes its decisions. The more explainable a model is, the easier it is to understand and validate its functioning and the results it produces.


Imagine we have a magical box that can guess if you like a toy just by looking at you. You’d want to know how it does that, right? Explainability is like asking the magic box to tell us its secret, to explain why it thinks you would like a particular toy.

In-depth explanation

Explainability in AI describes the degree to which a human can understand the outcome of a model or algorithm. It enables users and stakeholders to interpret machine learning output, making it an essential component of trustworthy AI. An explainable model allows people to grasp its functioning, the importance of input variables, and its decision-making process.

As AI is increasingly used in high-stakes areas - such as medicine, finance, and autonomous vehicles - explainability becomes crucial. A diagnosis made by an AI, for instance, requires the healthcare professional to understand the AI’s decision process to justify the prognosis to their patient and ensure its veracity.

There are various approaches to foster explainability. Visualization strategies provide graphical representations of how a model makes decisions. Feature importance rankings are often used, where the impact of each input variable on the output is scored and ranked. Surrogate models, which produce interpretable models approximating a black-box model’s functioning, are another technique. Each methodology has its strengths and weaknesses and is used according to the complexity of the model and the domain of application.

Explainability is not a silver bullet. Simplifying a complex model might lead to easy-to-understand models, but these explanations might be simplistic, incorrect, or biased, leading to ‘illusory transparency’. Trade-off exists between model accuracy and explainability: highly accurate models such as deep neural networks are often less interpretable.

Given these challenges, explainability is a vibrant research field, tied closely to issues of accountability, fairness, and effectiveness in AI systems, as it underpins the ability to verify algorithmic decisions.

Interpretability, Transparency, Trustworthy AI, Fairness, Machine Learning (ML),, Deep Learning, Neural Networks, Data Visualization, Surrogate Models, Feature Importance.