Explainable AI is an emerging subject of machine learning research that refers to strategies that try to provide transparency to typically opaque AI models and their predictions. It refers to a model’s projected impact and probable biases. It’s understandable: models might acquire unfavorable techniques to achieve goals on training data, or they can develop tendencies that can cause harm if left untreated.

Explainable AI supports the evaluation of model accuracy, fairness, transparency, and outcomes. When it comes to bringing AI models into production, an organization’s ability to explain AI is critical. Hence, XAI (Explainable AI) uses a variety of approaches and procedures to ensure that each choice made during the machine learning process can be traced and explained in a precise manner. AI, on the other hand, frequently uses an ML algorithm to arrive at a conclusion, but the AI system’s architects have little understanding of how the algorithm arrived at that result. This makes checking for correctness difficult and ultimately leads to a loss of control and responsibility.

As AI advances, humans will find it more difficult to grasp, intuit and retrace how the computer arrived at a conclusion. The entire computation is transformed into a “black box” that is hard to decipher. Many people think of neural networks as black boxes that are tough to interpret as Neural networks employed in deep learning are among the most difficult for humans to comprehend. Even the algorithm’s creators, the engineers and data scientists, have no idea what’s going on within them or how the AI algorithm arrived at a certain outcome.

Bias, which might be based on race, gender, age, or region, has long been a concern when developing AI models. Furthermore, because production data differs from training data, AI model performance might drift or deteriorate which is why it is necessary for the company to monitor and manage models in order to increase AI explainability while assessing the commercial effect of such algorithms. End-user trust, model auditability, and productive AI usage are all aided by explainable AI. It also reduces the risks of manufacturing AI in compliance, legal, security, and reputation.

With the increasing deployment of AI systems in high-stakes sectors such as healthcare, law, and banking, it is imperative to be cognizant of its explainability. Understanding how an AI-enabled system arrived at a certain result offers several advantages. Explainability can assist developers in ensuring that the system is operating as intended, it may be required to fulfill regulatory requirements, or it may be critical in allowing individuals affected by a decision to contest or amend the conclusion.

Why is the system making the decision it is making? What parts of the input are being used to make a final decision? What features of the input are most important in the decision? Will these features enable the model to operate accurately and consistently over time and in different operating conditions? Operators and end-users have been in the “unknown” due to the AI landscape filled with black-box solutions with limited autonomy, leading to vast mistrust in the public eye. At LENS, our solutions, while maintaining the necessary Intellectual Property Rights for our clients, customers, and end-users, include a guarantee to leverage everything at our disposal to ensure complete transparency, till all the “how” and “why” are answered satisfactorily.

We aim to build our models on a foundation of explainability, so individuals who are impacted by the technology can understand why decisions were made and adjust course as required to guarantee AI outputs are ethical, responsible, and fair.