One of the most urgent market challenges is not just achieving high accuracy levels in AI requests, but also understanding how and why models arrive at specific decisions.
This need has given rise to the field of Explainable AI (XAI), a discipline aimed at making models transparent and their decisions understandable to humans.
What is Explainable AI?
Explainable AI encompasses a set of methods and techniques that enable humans to understand, trust, and manage the outcomes generated by artificial intelligence systems.
It is not only about knowing the output of a model but interpreting the logical or probabilistic path that led to that decision.
Explainable AI is particularly relevant in contexts where automated decisions have direct impacts on individuals, such as healthcare, banking, recruitment, justice, or insurance.
Why is it so important?
There are numerous reasons why its use is significant for businesses; however, here we mention some of the most important ones:
- It helps in understanding how data was processed, enabling verification that the model behaves consistently, fairly, and logically.
- When users or stakeholders understand AI decisions, they are more likely to adopt and integrate them into their processes.
- Legislation such as the General Data Protection Regulation (GDPR) in Europe demands the “right to an explanation” for automated decisions.
- Explainability helps uncover hidden biases in data or models, correcting them before they cause real harm.
- Understanding how a model works allows identifying areas for improvement or refining the system’s training.
How is Explainable AI achieved?
Through the application of advanced techniques to support AI models:
- Inherently interpretable models: such as decision trees, linear regressions, or logistic regressions. Although they might sacrifice some accuracy, they are easier to explain.
- Post-hoc techniques: enable interpretation of more complex models, such as deep neural networks. Popular methods include:
- SHAP (SHapley Additive exPlanations): Estimates each variable’s contribution to a specific prediction.
- LIME (Local Interpretable Model-agnostic Explanations): Generates simple models to understand individual decisions of complex models.
- Attention visualizations: used in neural networks, such as those applied to natural language processing or computer vision models.
While Explainable AI serves various critical roles for businesses, it is essential for mitigating legal and reputational risks, facilitating decision audits, improving communication with users and customers, and understanding which variables most significantly impact decisions, thus optimizing strategies.
For this reason, in environments where algorithms make increasingly critical decisions, explainability is no longer optional but essential. Only AI that can be understood can truly be useful, ethical, and trustworthy.
Organizations investing in developing explainable artificial intelligence solutions will be better prepared to harness the potential of this technology, comply with regulations, and generate greater business value while having tools to respond proactively to various crises.
Would you like to discuss this further? Write to us at comunicaciones@bpt.com.co
