Currently, the two dominant most technologies in the world are machine learning (ML) and artificial intelligence (AI), as these aid numerous industries in resolving their business decisions. Therefore, to accelerate business-related decisions, IT professionals work on various business situations and develop data for AI and ML platforms.

The ML and AI platforms pick appropriate algorithms, provide answers based on predictions, and recommend solutions for your business; however, for the longest time, stakeholders have been worried about whether to trust AI and ML-based decisions, which has been a valid concern. Therefore, ML models are universally accepted as “black boxes,” as AI professionals could not once explain what happened to the data between the input and output.

However, the revolutionary concept of explainable AI (XAI) has transformed the way ML and AI engineering operate, making the process more convincing for stakeholders and AI professionals to implement these technologies into the business.

Three Considerations for Explainable AI

Mastering XAI helps IT professionals develop new technologies, streamline businesses, and provide transparency in data-driven decisions. Here are five exhibits on why you should consider XAI:

Building Trust and Adoption

The initial motive for considering XAI is to build the utmost trust between stakeholders; they need to feel confident that the AI ​​model that generates consequential decisions is performing accurately and fairly. Professionals who are dependent on AI applications should be aware that the next best recommendations or actions that come from a black box should help them make the right decisions, and they can follow them confidently.

Increasing Productivity

There are sets of tools and frameworks for XAI that can quickly detect errors and areas for improvement, making it easy for MLOps professionals to supervise the AI ​​systems, monitor them thoroughly, and effectively up and run them. For instance, understanding a specific feature of the AI ​​system that leads to an accurate model output helps IT professionals confirm if the patterns identified by the model are applicable in different areas and if they would be relevant enough to predict the future with data.

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