Why black box AI problem is bad for business - TechHQ

Deep learning algorithms take millions of data points as inputs, correlating specific features to produce an output. While humans are involved in the initial management of data, such as data labeling, once fed into a system the process is largely self-directed. Even for the data scientists and programs involved in the model's development, it can be difficult to interpret and subsequently explain how a process has led to a specific output. This is a complex issue earning the label'black box AI' -- and it is becoming a greater problem as artificial intelligence (AI) and machine learning plays a bigger role in our day-to-day and working lives. When the workings of software used for important operations and processes within a business cannot be easily viewed or understood, errors and bias can go unnoticed and snowball into much bigger, potentially irreparable, problems.