How can you use explainable AI in your business?

Explainable Artificial Intelligence (XAI) addresses the challenge posed by black box models of state-of-the-art models, making it difficult to understand their internal workings. XAI sheds light on the decision-making process of AI systems in a manner that is both understandable and interpretable for humans. By revealing the underlying information upon which actions are based, XAI justifies and reasons the model’s decisions, thereby enhancing transparency and fostering trust in AI systems.

XAI stands for Explainable AI. AI models uses business data for training, which can have diverse data and huge volume. The generated outcomes of any AI system is dependent on what data is utilize for the model training purpose. To justify the outcomes (results) and to ensure that business can rely on the same, the concept of XAI is crucial. It allows businesses to understand the process behind the results and enables stakeholders to communicate with clarity and insightful details.

In simple words, XAI is about making AI transparent and interpretable to us. It allows users to comprehend and trust the results and output created by machine learning algorithms. When using XAI, we can better understand an AI model’s expected impact and potential biases.

Explainable AI (XAI) is important for regulated industries because it helps to ensure that AI systems are transparent and accountable. In regulated industries, such as financial services and healthcare there are strict regulations that govern how AI systems can be used using mechanisms such as MRM (Model Risk Management). An important facet of MRM is that AI systems be able to explain their decisions, so that users can understand how they work and make informed decisions about how to use them.

AI is becoming an integral part of everyday life. the AI systems are used from simple chat to provide professional solutions such as generating agreements, policies, etc. The biggest issue in AI system is, the output generated by an AI system is not always correct. This is called AI hallucination. This happens due to insufficient training data or biasedness of the machine learning algorithm to come to a wrong conclusion or combine two non-related fields together and give us a wrong output. In this situation, final human judgment is always mandatory before making a decision based on the AI-generated results. In a situation like this, it is very much important to know how AI made the decision. the XAI provides an answer to this issue

One mechanism of adopting XAI is through Google Vertex AI Explainability, which is a suite of tools that help organizations understand and interpret the predictions made by machine learning models. These could be Feature-based explanations, which show how much each feature in the model contributed to the predictions for each given instance. And, Example-based explanations: These explanations show how similar predictable instances are to the instance similar in function to the use case at hand.

Explainable AI is a tool for developing trust and transparency in AI systems. For eg, in Clinical or medical field. Doctors would love to know how AI make predictions on human x rays , so doctor n patient can make life saving decision. With out Explainable AI, your AI system is a black box in healthcare domain, it’s hard to completely depends on Life saving AI systems predictions or results. It significantly varies from field to field. For enterprises, you can use explainable AI to understand the performance of model and modify input feature sets for better performance.

The reluctance of senior executives to embrace the AI system in my organization is mainly because the AI system is a "black box" and nobody knows how the answer is provided and where our data is processed. Since XAI provides answers to these issues, XAI can be used in most of the functionalities in the organization.

One of the biggest limitations either in AI or XAI is having lack of training data. Because machine learning algorithms need a larger dataset to make correct output. For some businesses, in the local Asian context, AI systems are still incapable of providing specific answers because of not having enough training data. And also, I am agree with the explanation that adopting XAI system is not an easy task or cheep solution in majority of business in my region

Yes, there are lots of materials and resources out there for the people who are interested. AI growing too fast and it will take some time to digest for an ordinary organization like ours. But, as professionals who are in the field and contribute to the field, we need to be ready with the necessary knowledge. There are lots of resources out there.

Today with the growth in AI-enabled solutions and the rise in Generative AI applications, it is necessary to understand the process rather than the outcomes. To get clarity on the process of how data is utilized, processed, and fed into the model for deriving outcomes based on business goals, XAI comes into existence. The concept of Explainable AI (XAI) not only helps in relying on the AI system but also offers additional insights and metrics that can be used by the business. For areas where XAI plays a crucial role: 1. Healthcare & Life Sciences 2. Banking, Financial Services, & Insurance (BFSI) 3. Retail & CPG 4. Manufacturing 5. Energy & Utilities 6. Education XAI will help in building trust in AI systems.

Explainable AI (XAI) can be valuable in business by enhancing transparency and trust in AI-driven decisions. By utilizing XAI techniques, you can provide understandable explanations for how AI models arrive at specific conclusions. This helps stakeholders, such as customers and regulatory bodies, to comprehend the reasoning behind predictions and enables better decision-making. Incorporating XAI can also aid in identifying biases, improving model robustness, and ensuring compliance with regulations.