3 ways to effectively demystify the AI black box

Artificial intelligence has demonstrated immense promise when applying machine learning to support the overall processing of large datasets, particularly in the banking and financial services industry. Sixty percent of financial services companies have implemented at least one form of AI, ranging from virtual assistants communicating with customers and the automation of workflows to managing fraud and network security. Despite these advancements in efficiency and automation, complexities from the inner workings of AI models often create a "black box" issue. This largely stems from lack of understanding of how the system works and a continual concern around opacity, unfair discrimination, ethics and dangers to privacy and autonomy. In fact, the lack of transparency in system operation is frequently linked to hidden biases.