Explainable AI, the added value to AI-based analytics

More and more institutions are wondering about the jurisprudence related to Artificial Intelligence management and its applications. Explainable AI (XAI) is a set of processes that allows to make understandable the outputs of the performed analyses and to reconstruct the process that led to the specific result in order to make it even more acceptable. Regarding application within companies and decision-making processes, the goal is to win the trust of managers and entrepreneurs, moving as far away from the “black box” concept as possible.
Explainable AI is used to describe a model, its expected impact and potential errors. It also helps to circumscribe accuracy, transparency and outcomes in decision-making processes, becoming critical in supporting the adoption of a controlled approach to AI development.
As technologies and models evolve, understanding how an AI-powered system achieves a specific output has several benefits. Explainability can help developers to ensure that the system works as expected, to meet regulatory standards and finally to enable the subjects affected by a decision to review that outcome.

A company must have a complete understanding of the decision-making processes applied through model monitoring and the accountability of them, and not to blindly trust them. Explainable AI can indeed help humans understand and explain machine learning (ML) algorithms, deep learning, and neural networks.
The technology that allows explainability is called causal machine learning, also known as Causal AI, which enables the identification and reconstruction of cause-and-effect relationships between variables involved in the processes.
Explainable AI is one of the key requirements to implement accountable AI, a methodology for large-scale implementation of AI methods in real organizations that includes equity, model explainability and accountability.
With explainable AI, an enterprise can solve and improve model performance by helping stakeholders at different levels to understand AI model behaviors. Continuous model evaluation enables companies to compare model predictions, quantify model risk and optimize model performance. An AI-based platform can generate feature attributions for model predictions and enable teams to visually analyze model performance with interactive graphs and exportable documents.

Few companies using Artificial Intelligence and prescriptive models have so far taken the next step in increasing the comprehensibility and confidence in the results and action suggestions offered by AI. Leveraging the potential of Natural Language Generation and NLP software, some software houses have begun to provide complex analysis outputs in a comprehensible language for their end users.
Natural Language Generation refers to the set of techniques and algorithms for the automatic generation of information written in natural language, which means spoken language. Indeed, in these solutions the output does not only result in the display of graphs and tables, but also through suggestions and directions provided in a textual language, making the output even more effective and understandable.